title: Mayank Agarwal

text: He was the leading run-scorer for Karnataka in the 2018–19 Vijay Hazare Trophy, with 251 runs in seven matches. In October 2018, he was named in India B's squad for the 2018–19 Deodhar Trophy. The following month, he was named as one of eight players to watch ahead of the 2018–19 Ranji Trophy. In October 2019, he was named in India C's squad for the 2019–20 Deodhar Trophy.


title: Mayank Agarwal

text: On 27 September 2020, Mayank scored his maiden Indian Premier League century for Kings XI Punjab against Rajasthan Royals in Sharjah Cricket Stadium. He scored 106 off 50 balls in the match but ended up on the losing side. He scored a total of 424 runs in 11 matches for Kings XI Punjab at an average of 38.54 in IPL 2020.


title: Mayank Agarwal

text: Agarwal became the 13th captain of the Punjab Kings on 2 May 2021, when he captained the team against Delhi Capitals in the absence of the regular captain KL Rahul, who had undergone appendicitis surgery. He scored a 99*, making him the third batsman in the IPL to score 99 not out, and was awarded the Man of the Match award although the team ended up on the losing side.


title: Mayank Agarwal

text: On 28 February 2022, He was appointed as a captain of Punjab Kings and became 12th full time captain of the franchise.


title: Mayank Agarwal

text: In September 2018, he was named in India's Test squad for their series against the West Indies, but he did not play. In December 2018, he was added to India's Test squad for their series against Australia, after Prithvi Shaw was ruled out of the side due to ankle injury. He made his Test debut against Australia on 26 December 2018, scoring seventy-six runs in his first innings at the Melbourne Cricket Ground. This was the highest score by an Indian cricketer on a Test debut in Australia, going past the previous record of 51 runs set by Dattu Phadkar, at the Sydney Cricket Ground (SCG) in 1947. He played the 4th test also and finished the series with 195 runs.


title: Mayank Agarwal

text: In July 2019, he was added to India's squad for the 2019 Cricket World Cup, replacing Vijay Shankar, who was ruled out of the rest of the tournament due to toe injury.


title: Mayank Agarwal

text: In October 2019, in the first Test match against South Africa, Agarwal scored his maiden century in Test cricket. He went on to convert his maiden test century into his first double hundred in a Test match, before being dismissed for 215 runs of 371 balls with 23 fours and 6 sixes. After hitting his 2nd Test hundred against South Africa, Agarwal became only the 2nd Indian opener after Virender Sehwag (2009–10) to score back to back centuries against South Africa.


title: Mayank Agarwal

text: In November 2019, Mayank Agarwal hit his second double century in only his eighth Test match, at Indore against Bangladesh, recording his current highest score of 243 in 330 deliveries with eight sixes. He broke the record of Donald Bradman to become the second-fastest batsman to score two double hundreds, having achieved this in 12 innings. The following month, he was added to India's One Day International (ODI) squad for the series against the West Indies, replacing the injured Shikhar Dhawan. He was named in India's squad for their Test and ODI series against New Zealand. He made his ODI debut for India, against New Zealand, on 5 February 2020. In October 2020, Mayank was selected in the T20I, ODI and Test squad for the Australian tour. On 19 December 2020, in the first Test against Australia, he scored his 1,000th run in Test matches, becoming the third fastest Indian batsman to reach 1000 runs in Test.


title: Mayank Agarwal

text: Mayank Agarwal scored 857 runs, the fourth-highest by an Indian batsman in the inaugural WTC cycle, but lost his place to Gill after failing in three matches in Australia.


title: Mayank Agarwal

text: He made his comeback in home series against New Zealand, In second test match he scored 150 in First innings And 62 in second innings for which he was awarded the man of the match. He retained his place for next series against South Africa in the team as Rohit Sharma was ruled out due to hamstring injury. During this series, he started well with a well-constructed fifty in the first test, but struggled to make a substantial score thereafter in difficult batting conditions.


title: Mayank Agarwal

text: In February 2022, Agarwal was drafted in as replacement for the ODIs against West Indies owing to the unavailability of few players due to covid and personal reasons.


title: Mayank Agarwal

text: Agarwal practices the meditation technique of Vipassanā, being introduced to it by his father Anurag Agarwal. He is also said to have been inspired by the Joseph Murphy book "The Power of the Subconscious Mind".


title: Mayank Agarwal

text: In January 2018, Agarwal got engaged to Aashita Sood, daughter of Indian Police Service officer Praveen Sood, current Head of Police Force in Karnataka and married her on 6 June 2018. The couple had their first child, a boy on 8 December 2022.


title: Rock 'n' Roll High School

text: Rock 'n' Roll High School is a 1979 American musical comedy film directed by Allan Arkush, produced by Michael Finnell, and starring P. J. Soles, Vince Van Patten, Clint Howard, and Dey Young. The film featured the punk rock group Ramones.


title: Rock 'n' Roll High School

text: The film is set in 1980. Vince Lombardi High School keeps losing principals to nervous breakdowns because of the students' love of rock 'n' roll and their disregard for education. The leader of the students, Riff Randell, is the biggest Ramones fan at the school and also the worst behavioral problem, in that her disciplinary record fills an entire filing cabinet. She waits in line for three days to get tickets to see the band, hoping to meet Joey Ramone so she can give him a song she wrote for the band, "Rock 'n' Roll High School".


title: Rock 'n' Roll High School

text: When the tyrannical Principal Togar takes her ticket away, Riff and her best friend Kate Rambeau have to find another way to meet their heroes: winning a radio contest. Riff succeeds in delivering her song to Joey Ramone, but the next day Principal Togar and a group of parents attempt to burn a pile of rock records. In response, the students, joined by the Ramones (who are made honorary students), overthrow the teachers and hall monitors to take over the high school, with Principal Togar asking the musicians "Do your parents know you're Ramones?" When the police are summoned and demand that the students evacuate the building, they do so, but then the students and the Ramones burn down the school as a final act of youthful rebellion.


title: Rock 'n' Roll High School

text: Roger Corman, executive producer of the film, was looking to make a modern teen film similar to the ones he made in his early career during the 1960s, with the focus on current music of the time. The initial title "Disco High" was selected for a story idea from Allan Arkush and Joe Dante. A script was developed by Richard Whitley, Russ Dvonch, and Joseph McBride. During this time, the film went through several different title changes including "Heavy Metal Kids" and "Girl's Gym". Arkush directed the majority of the film, but Dante also helped when Arkush was suffering from exhaustion.


title: Rock 'n' Roll High School

text: Corman originally wanted Cheap Trick or Todd Rundgren to play the band, but due to a conflict of schedules, he was forced to find an alternative. The Ramones were suggested by Paul Bartel, one of the actors in the film.


title: Rock 'n' Roll High School

text: The genesis for the plot was a favorite story told to the film's original writer by his father, Raymond E. McBride of the "Milwaukee Journal", who staged a walkout from his Superior (Wis.) Central High school in the 1920s.


title: Rock 'n' Roll High School

text: The film was shot on the campus of the defunct Mount Carmel High School in South Central Los Angeles, that had been closed in 1976. The school's actual demolition was used for the end of the film. The nighttime school explosions and fires were so great that many were scared away by and, temporarily, would not return to the on campus sets. Another location used for filming was Mira Costa High School in Manhattan Beach, California. The American football uniforms and cheer-leading outfits were those from MCHS.


title: Rock 'n' Roll High School

text: "Rock 'n' Roll High School" was originally released on VHS by Warner Home Video in 1983, and was later re-released on VHS in 1996 by New Horizons Home Video (). A year later, in 1997, it was issued on DVD by Lumivision. A second DVD release occurred in 1999 from Slingshot. Shortly after Joey Ramone's death in 2001, New Concorde produced a third DVD release. The film was once again issued on DVD in 2005 by Buena Vista Home Entertainment ( ). DVDs in the PAL format were issued by Umbrella Entertainment in 2003 () and again in 2007 ().


title: Rock 'n' Roll High School

text: The film was a part of Shout! Factory's "Roger Corman Cult Classics" series, reissued on DVD in May 2010. Shout! Factory released the film with exclusive content on Blu-ray on May 11, 2010 and again on November 19, 2019, with a new 4K restoration.


title: Rock 'n' Roll High School

text: A soundtrack album on Sire/Warner Bros. Records was released around the same time, but it included only a limited number of songs from the film. The two main Ramones songs (the title song and "I Want You Around") were recorded by Ed Stasium but remixed by Phil Spector for the soundtrack album. The original Ed Stasium mixes were not issued until the compilation album "Ramones Mania" (1988) and the compilation album "" (1999), respectively.


title: Rock 'n' Roll High School

text: "Rock 'n' Roll High School" received generally positive reviews and has an 81% rating at the review aggregator website Rotten Tomatoes from 26 film critics.


title: Rock 'n' Roll High School

text: On July 31, 2008, it was announced that actor/writer Alex Winter had been hired to script a remake of the film for Howard Stern's production company.


title: Rock 'n' Roll High School

text: Corman's short-lived comic book publishing imprint, Roger Corman's Cosmic Comics, released a two-issue take on the film in 1995, written by Bob Fingerman with art by Shane Oakley and Jason Lutes. Unable to acquire likeness licenses for the Ramones, the comic instead featured the Melvins.


title: Association rule learning

text: Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. In any given transaction with a variety of items, association rules are meant to discover the rules that determine how or why certain items are connected.


title: Association rule learning

text: Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. For example, the rule formula_1 found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as, e.g., promotional pricing or product placements.


title: Association rule learning

text: In addition to the above example from market basket analysis association rules are employed today in many application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions.


title: Association rule learning

text: The association rule algorithm itself consists of various parameters that can make it difficult for those without some expertise in data mining to execute, with many rules that are arduous to understand.


title: Association rule learning

text: Following the original definition by Agrawal, Imieliński, Swami the problem of association rule mining is defined as:


title: Association rule learning

text: Each "transaction" in formula_5 has a unique transaction ID and contains a subset of the items in formula_6.


title: Association rule learning

text: In Agrawal, Imieliński, Swami a "rule" is defined only between a set and a single item, formula_9 for formula_10.


title: Association rule learning

text: Every rule is composed by two different sets of items, also known as "itemsets", formula_11 and formula_12, where formula_11 is called "antecedent" or left-hand-side (LHS) and formula_12 "consequent" or right-hand-side (RHS). The antecedent is that item that can be found in the data while the consequent is the item found when combined with the antecedent. The statement formula_7 is often read as "if formula_11 then formula_12", where the antecedent (formula_11 ) is the "if" and the consequent (formula_12) is the "then". This simply implies that, in theory, whenever formula_11 occurs in a dataset, then formula_12 will as well.


title: Association rule learning

text: Association rules are made by searching data for frequent if-then patterns and by using a certain criterion under Support and Confidence to define what the most important relationships are. Support is the evidence of how frequent an item appears in the data given, as Confidence is defined by how many times the if-then statements are found true. However, there is a third criteria that can be used, it is called Lift and it can be used to compare the expected Confidence and the actual Confidence. Lift will show how many times the if-then statement is expected to be found to be true.


title: Association rule learning

text: Association rules are made to calculate from itemsets, which are created by two or more items. If the rules were built from the analyzing from all the possible itemsets from the data then there would be so many rules that they wouldn’t have any meaning. That is why Association rules are typically made from rules that are well represented by the data.


title: Association rule learning

text: There are many different data mining techniques you could use to find certain analytics and results, for example, there is Classification analysis, Clustering analysis, and Regression analysis. What technique you should use depends on what you are looking for with your data. Association rules are primarily used to find analytics and a prediction of customer behavior. For Classification analysis, it would most likely be used to question, make decisions, and predict behavior. Clustering analysis is primarily used when there are no assumptions made about the likely relationships within the data. Regression analysis Is used when you want to predict the value of a continuous dependent from a number of independent variables.


title: Association rule learning

text: There are many benefits of using Association rules like finding the pattern that helps understand the correlations and co-occurrences between data sets. A very good real-world example that uses Association rules would be medicine. Medicine uses Association rules to help diagnose patients. When diagnosing patients there are many variables to consider as many diseases will share similar symptoms. With the use of the Association rules, doctors can determine the conditional probability of an illness by comparing symptom relationships from past cases.


title: Association rule learning

text: However, Association rules also lead to many different downfalls such as finding the appropriate parameter and threshold settings for the mining algorithm. But there is also the downfall of having a large number of discovered rules. The reason is that this does not guarantee that the rules will be found relevant, but it could also cause the algorithm to have low performance. Sometimes the implemented algorithms will contain too many variables and parameters. For someone that doesn’t have a good concept of data mining, this might cause them to have trouble understanding it.


title: Association rule learning

text: ThresholdsWhen using Association rules, you are most likely to only use Support and Confidence. However, this means you have to satisfy a user-specified minimum support and a user-specified minimum confidence at the same time. Usually, the Association rule generation is split into two different steps that needs to be applied:


title: Association rule learning

text: The Table on the left is the original unorganized data and the table on the right is organized by the thresholds. In this case Item C is better than the thresholds for both Support and Confidence which is why it is first. Item A is second because its threshold values are spot on. Item D has met the threshold for Support but not Confidence. Item B has not met the threshold for either Support or Confidence and that is why it is last.


title: Association rule learning

text: To find all the frequent itemsets in a database is not an easy task since it involves going through all the data to find all possible item combinations from all possible itemsets. The set of possible itemsets is the power set over and has size formula_22 , of course this means to exclude the empty set which is not considered to be a valid itemset. However, the size of the power set will grow exponentially in the number of item that is within the power set . An efficient search is possible by using the downward-closure property of support (also called "anti-monotonicity"). This would guarantee that a frequent itemset and all its subsets are also frequent and thus will have no infrequent itemsets as a subset of a frequent itemset. Exploiting this property, efficient algorithms (e.g., Apriori and Eclat) can find all frequent itemsets.


title: Association rule learning

text: To illustrate the concepts, we use a small example from the supermarket domain. Table 2 shows a small database containing the items where, in each entry, the value 1 means the presence of the item in the corresponding transaction, and the value 0 represents the absence of an item in that transaction. The set of items is formula_23.


title: Association rule learning

text: An example rule for the supermarket could be formula_24 meaning that if butter and bread are bought, customers also buy milk.


title: Association rule learning

text: In order to select interesting rules from the set of all possible rules, constraints on various measures of significance and interest are used. The best-known constraints are minimum thresholds on support and confidence.


title: Association rule learning

text: Let formula_25 be itemsets, formula_7 an association rule and a set of transactions of a given database.


title: Association rule learning

text: Note: this example is extremely small. In practical applications, a rule needs a support of several hundred transactions before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.


title: Association rule learning

text: In our example, it can be easier to explain support by writing formula_27 where A and B are separate item sets that occur in at the same time in a transaction.


title: Association rule learning

text: Using Table 2 as an example, the itemset formula_28 has a support of formula_29 since it occurs in 20% of all transactions (1 out of 5 transactions). The argument of "support of X" is a set of preconditions, and thus becomes more restrictive as it grows (instead of more inclusive).


title: Association rule learning

text: Furthermore, the itemset formula_30 has a support of formula_29 as it appears in 20% of all transactions as well.


title: Association rule learning

text: When using antecedents and consequents, it allows a data miner to determine the support of multiple items being bought together in comparison to the whole data set. For example, Table 2 shows that if milk is bought, then bread is bought has a support of 0.4 or 40%. This because in 2 out 5 of the transactions, milk as well as bread are bought. In smaller data sets like this example, it is harder to see a strong correlation when there are few samples, but when the data set grows larger, support can be used to find correlation between two or more products in the supermarket example.


title: Association rule learning

text: If we set the support threshold to ≥0.4 in Table 3, then the formula_32 would be removed since it did not meet the minimum threshold of 0.4. Minimum threshold is used to remove samples where there is not a strong enough support or confidence to deem the sample as important or interesting in the dataset.


title: Association rule learning

text: Another way of finding interesting samples is to find the value of (support)X(confidence); this allows a data miner to see the samples where support and confidence are high enough to be highlighted in the dataset and prompt a closer look at the sample to find more information on the connection between the items.


title: Association rule learning

text: Support can be beneficial for finding the connection between products in comparison to the whole dataset, whereas confidence looks at the connection between one or more items and another item. Below is a table that shows the comparison and contrast between support and support x confidence, using the information from Table 4 to derive the confidence values.


title: Association rule learning

text: The support of with respect to is defined as the proportion of transactions in the dataset which contains the itemset . Denoting a transaction by formula_33 where is the unique identifier of the transaction and is its itemset, the support may be written as:


title: Association rule learning

text: This notation can be used when defining more complicated datasets where the items and itemsets may not be as easy as our supermarket example above. Other examples of where support can be used is in finding groups of genetic mutations that work collectively to cause a disease, investigating the number of subscribers that respond to upgrade offers, and discovering which products in a drug store are never bought together.


title: Association rule learning

text: With respect to , the confidence value of an association rule, often denoted as formula_7, is the ratio of transactions containing both and to the total amount of values present, where is the antecedent and is the consequent.


title: Association rule learning

text: Confidence can also be interpreted as an estimate of the conditional probability formula_36, the probability of finding the RHS of the rule in transactions under the condition that these transactions also contain the LHS.


title: Association rule learning

text: The equation illustrates that confidence can be computed by calculating the co-occurrence of transactions and within the dataset in ratio to transactions containing only . This means that the number of transactions in both and is divided by those just in .


title: Association rule learning

text: For example, Table 2 shows the rule formula_24 which has a confidence of formula_39 in the dataset, which denotes that every time a customer buys butter and bread, they also buy milk. This particular example demonstrates the rule being correct 100% of the time for transactions containing both butter and bread. The rule formula_40, however, has a confidence of formula_41. This suggests that eggs are bought 67% of the times that fruit is brought. Within this particular dataset, fruit is purchased a total of 3 times, with two of those times consisting of egg purchases.


title: Association rule learning

text: For larger datasets, a minimum threshold, or a percentage cutoff, for the confidence can be useful for determining item relationships. When applying this method to some of the data in Table 2, information that does not meet the requirements are removed. Table 4 shows association rule examples where the minimum threshold for confidence is 0.5 (50%). Any data that does not have a confidence of at least 0.5 is omitted. Generating thresholds allow for the association between items to become stronger as the data is further researched by emphasizing those that co-occur the most. The table uses the confidence information from Table 3 to implement the Support x Confidence column, where the relationship between items via their both confidence and support, instead of just one concept, is highlighted. Ranking the rules by Support x Confidence multiples the confidence of a particular rule to its support and is often implemented for a more in-depth understanding of the relationship between the items.


title: Association rule learning

text: Overall, using confidence in association rule mining is great way to bring awareness to data relations. Its greatest benefit is highlighting the relationship between particular items to one another within the set, as it compares co-occurrences of items to the total occurrence of the antecedent in the specific rule. However, confidence is not the optimal method for every concept in association rule mining. The disadvantage of using it is that it does not offer multiple difference outlooks on the associations. Unlike support, for instance, confidence does not provide the perspective of relationships between certain items in comparison to the entire dataset, so while milk and bread, for example, may occur 100% of the time for confidence, it only has a support of 0.4 (40%). This is why it is important to look at other viewpoints, such as Support x Confidence, instead of solely relying on one concept incessantly to define the relationships.


title: Association rule learning

text: If the rule had a lift of 1, it would imply that the probability of occurrence of the antecedent and that of the consequent are independent of each other. When two events are independent of each other, no rule can be drawn involving those two events.


title: Association rule learning

text: If the lift is > 1, that lets us know the degree to which those two occurrences are dependent on one another, and makes those rules potentially useful for predicting the consequent in future data sets.


title: Association rule learning

text: If the lift is < 1, that lets us know the items are substitute to each other. This means that presence of one item has negative effect on presence of other item and vice versa.


title: Association rule learning

text: For example, the rule formula_43 has a conviction of formula_47, and can be interpreted as the ratio of the expected frequency that X occurs without Y (that is to say, the frequency that the rule makes an incorrect prediction) if X and Y were independent divided by the observed frequency of incorrect predictions. In this example, the conviction value of 1.2 shows that the rule formula_43 would be incorrect 20% more often (1.2 times as often) if the association between X and Y was purely random chance.


title: Association rule learning

text: In addition to confidence, other measures of "interestingness" for rules have been proposed. Some popular measures are:


title: Association rule learning

text: Several more measures are presented and compared by Tan et al. and by Hahsler. Looking for techniques that can model what the user has known (and using these models as interestingness measures) is currently an active research trend under the name of "Subjective Interestingness."


title: Association rule learning

text: The concept of association rules was popularized particularly due to the 1993 article of Agrawal et al., which has acquired more than 23,790 citations according to Google Scholar, as of April 2021, and is thus one of the most cited papers in the Data Mining field. However, what is now called "association rules" is introduced already in the 1966 paper on GUHA, a general data mining method developed by Petr Hájek et al.


title: Association rule learning

text: An early (circa 1989) use of minimum support and confidence to find all association rules is the Feature Based Modeling framework, which found all rules with formula_49 and formula_50 greater than user defined constraints.


title: Association rule learning

text: One limitation of the standard approach to discovering associations is that by searching massive numbers of possible associations to look for collections of items that appear to be associated, there is a large risk of finding many spurious associations. These are collections of items that co-occur with unexpected frequency in the data, but only do so by chance. For example, suppose we are considering a collection of 10,000 items and looking for rules containing two items in the left-hand-side and 1 item in the right-hand-side. There are approximately 1,000,000,000,000 such rules. If we apply a statistical test for independence with a significance level of 0.05 it means there is only a 5% chance of accepting a rule if there is no association. If we assume there are no associations, we should nonetheless expect to find 50,000,000,000 rules. Statistically sound association discovery controls this risk, in most cases reducing the risk of finding "any" spurious associations to a user-specified significance level.


title: Association rule learning

text: Some well-known algorithms are Apriori, Eclat and FP-Growth, but they only do half the job, since they are algorithms for mining frequent itemsets. Another step needs to be done after to generate rules from frequent itemsets found in a database.


title: Association rule learning

text: Apriori is given by R. Agrawal and R. Srikant in 1994 for frequent item set mining and association rule learning. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often. The name of the algorithm is Apriori because it uses prior knowledge of frequent itemset properties.


title: Association rule learning

text: Overview: Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time (a step known as "candidate generation"), and groups of candidates are tested against the data. The algorithm terminates when no further successful extensions are found. Apriori uses breadth-first search and a Hash tree structure to count candidate item sets efficiently. It generates candidate item sets of length  from item sets of length . Then it prunes the candidates which have an infrequent sub pattern. According to the downward closure lemma, the candidate set contains all frequent -length item sets. After that, it scans the transaction database to determine frequent item sets among the candidates.


title: Association rule learning

text: Example: Assume that each row is a cancer sample with a certain combination of mutations labeled by a character in the alphabet. For example a row could have {a, c} which means it is affected by mutation 'a' and mutation 'c'.


title: Association rule learning

text: Now we will generate the frequent item set by counting the number of occurrences of each character. This is also known as finding the support values. Then we will prune the item set by picking a minimum support threshold. For this pass of the algorithm we will pick 3.


title: Association rule learning

text: Since all support values are three or above there is no pruning. The frequent item set is {a}, {b}, {c}, and {d}. After this we will repeat the process by counting pairs of mutations in the input set.


title: Association rule learning

text: Now we will make our minimum support value 4 so only {a, d} and {c, d} will remain after pruning. Now we will use the frequent item set to make combinations of triplets. We will then repeat the process by counting occurrences of triplets of mutations in the input set.


title: Association rule learning

text: Since we only have one item the next set of combinations of quadruplets is empty so the algorithm will stop.


title: Association rule learning

text: Apriori has some limitations. Candidate generation can result in large candidate sets. For example a 10^4 frequent 1-itemset will generate a 10^7 candidate 2-itemset. The algorithm also needs to frequently scan the database, to be specific n+1 scans where n is the length of the longest pattern. Apriori is slower than the Eclat algorithm. However, Apriori performs well compared to Eclat when the dataset is large. This is because in the Eclat algorithm if the dataset is too large the tid-lists become too large for memory. FP-growth outperforms the Apriori and Eclat. This is due to the FP-growth algorithm not having candidate generation or test, using a compact data structure, and only having one database scan.


title: Association rule learning

text: Eclat (alt. ECLAT, stands for Equivalence Class Transformation) is a backtracking algorithm, which traverses the frequent itemset lattice graph in a depth-first search (DFS) fashion. Whereas the breadth-first search (BFS) traversal used in the Apriori algorithm will end up checking every subset of an itemset before checking it, DFS traversal checks larger itemsets and can save on checking the support of some of its subsets by virtue of the downward-closer property. Furthermore it will almost certainly use less memory as DFS has a lower space complexity than BFS.


title: Association rule learning

text: To illustrate this, let there be a frequent itemset {a, b, c}. a DFS may check the nodes in the frequent itemset lattice in the following order: {a} → {a, b} → {a, b, c}, at which point it is known that {b}, {c}, {a, c}, {b, c} all satisfy the support constraint by the downward-closure property. BFS would explore each subset of {a, b, c} before finally checking it. As the size of an itemset increases, the number of its subsets undergoes combinatorial explosion.


title: Association rule learning

text: In the first pass, the algorithm counts the occurrences of items (attribute-value pairs) in the dataset of transactions, and stores these counts in a 'header table'. In the second pass, it builds the FP-tree structure by inserting transactions into a trie.


title: Association rule learning

text: Items in each transaction have to be sorted by descending order of their frequency in the dataset before being inserted so that the tree can be processed quickly.


title: Association rule learning

text: If many transactions share most frequent items, the FP-tree provides high compression close to tree root.


title: Association rule learning

text: Recursive processing of this compressed version of the main dataset grows frequent item sets directly, instead of generating candidate items and testing them against the entire database (as in the apriori algorithm).


title: Association rule learning

text: Growth begins from the bottom of the header table i.e. the item with the smallest support by finding all sorted transactions that end in that item. Call this item formula_6.


title: Association rule learning

text: A new conditional tree is created which is the original FP-tree projected onto formula_6. The supports of all nodes in the projected tree are re-counted with each node getting the sum of its children counts. Nodes (and hence subtrees) that do not meet the minimum support are pruned. Recursive growth ends when no individual items conditional on formula_6 meet the minimum support threshold. The resulting paths from root to formula_6 will be frequent itemsets. After this step, processing continues with the next least-supported header item of the original FP-tree.


title: Association rule learning

text: Once the recursive process has completed, all frequent item sets will have been found, and association rule creation begins.


title: Association rule learning

text: The ASSOC procedure is a GUHA method which mines for generalized association rules using fast bitstrings operations. The association rules mined by this method are more general than those output by apriori, for example "items" can be connected both with conjunction and disjunctions and the relation between antecedent and consequent of the rule is not restricted to setting minimum support and confidence as in apriori: an arbitrary combination of supported interest measures can be used.


title: Association rule learning

text: OPUS is an efficient algorithm for rule discovery that, in contrast to most alternatives, does not require either monotone or anti-monotone constraints such as minimum support. Initially used to find rules for a fixed consequent it has subsequently been extended to find rules with any item as a consequent. OPUS search is the core technology in the popular Magnum Opus association discovery system.


title: Association rule learning

text: A famous story about association rule mining is the "beer and diaper" story. A purported survey of behavior of supermarket shoppers discovered that customers (presumably young men) who buy diapers tend also to buy beer. This anecdote became popular as an example of how unexpected association rules might be found from everyday data. There are varying opinions as to how much of the story is true. Daniel Powers says:


title: Association rule learning

text: In 1992, Thomas Blischok, manager of a retail consulting group at Teradata, and his staff prepared an analysis of 1.2 million market baskets from about 25 Osco Drug stores. Database queries were developed to identify affinities. The analysis "did discover that between 5:00 and 7:00 p.m. that consumers bought beer and diapers". Osco managers did NOT exploit the beer and diapers relationship by moving the products closer together on the shelves.


title: Association rule learning

text: Multi-Relation Association Rules: Multi-Relation Association Rules (MRAR) are association rules where each item may have several relations. These relations indicate indirect relationship between the entities. Consider the following MRAR where the first item consists of three relations "live in", "nearby" and "humid": “Those who "live in" a place which is "nearby" a city with "humid" climate type and also are "younger" than 20 -> their "health condition" is good”. Such association rules are extractable from RDBMS data or semantic web data.


title: Association rule learning

text: Contrast set learning is a form of associative learning. Contrast set learners use rules that differ meaningfully in their distribution across subsets.


title: Association rule learning

text: Weighted class learning is another form of associative learning in which weight may be assigned to classes to give focus to a particular issue of concern for the consumer of the data mining results.


title: Association rule learning

text: High-order pattern discovery facilitate the capture of high-order (polythetic) patterns or event associations that are intrinsic to complex real-world data.


title: Association rule learning

text: K-optimal pattern discovery provides an alternative to the standard approach to association rule learning that requires that each pattern appear frequently in the data.


title: Association rule learning

text: Approximate Frequent Itemset mining is a relaxed version of Frequent Itemset mining that allows some of the items in some of the rows to be 0.


title: Association rule learning

text: Sequential pattern mining discovers subsequences that are common to more than minsup sequences in a sequence database, where minsup is set by the user. A sequence is an ordered list of transactions.


title: Association rule learning

text: Subspace Clustering, a specific type of Clustering high-dimensional data, is in many variants also based on the downward-closure property for specific clustering models.


title: Association rule learning

text: Warmr is shipped as part of the ACE data mining suite. It allows association rule learning for first order relational rules.


8 Mile: Music from and Inspired by the Motion Picture is the official soundtrack to the 2002 film of the same name. The album, performed by various artists, was released on Shady Records. It spawned the hit single "Lose Yourself" by Eminem, who also stars in the semi-autobiographical movie.


title: 8 Mile: Music from and Inspired by the Motion Picture

text: The album also spawned a follow-up soundtrack, More Music from 8 Mile, consisting of songs that appear in the film and were released as singles during the film's time setting of 1995. One of the songs was performed by 2Pac, who would be the subject of a with a , who also produced a posthumous album by 2Pac. The album also features four songs by Wu-Tang Clan and its members, and two songs by Mobb Deep, who eventually signed to G-Unit Records. Both albums were also made available in censored versions, removing most of the strong language and sexual and violent content.


text: "8 Mile: Music from and Inspired by the Motion Picture" debuted at number one on the " Billboard" 200, selling over 700,000 copies in its first week. It sold 510,000 copies in its second week and eventually became the fifth best-selling album in the US of 2002, with sales of 3.4 million copies. It is certified quadruple platinum by the Recording Industry Association of America (RIAA). The album featured the universal number-one hit "Lose Yourself", which won the Oscar for Best Original Song.


text: "Lose Yourself" was released as the soundtrack's lead single on October 28, 2002. "Rap Name" was released as the soundtrack's second single six days earlier, which is October 22 of that same year. "Wanksta" was released as the soundtrack's third single on November 5. "8 Mile" was released as the soundtrack's fourth single on December 15.


text: The clean version of the "8 Mile" soundtrack removes most of the strong language and sexual and violent content. The only word left uncensored on the soundtrack, is the word "ass" (except on "Places to Go" by 50 Cent, where the word "ass" is used twice, but the word was only censored once). No other words are uncensored on the clean version of "8 Mile".


text: "That's My Nigga Fo' Real", by rapper Young Zee, is listed as "That's My ***** Fo' Real" on the clean version. In "Rap Game" by D12 featuring 50 Cent in Proof's verse, the word "shit" is uncensored (even on the clean version), and in Eminem's verse, the words "White House" and "Cheney" are censored on both edited and explicit versions. A very rare version of "Rap Game" can be heard on the internet, in which the words "White House" and "Cheney" are left uncensored. In "Rabbit Run" by Eminem, the word "fuck" was left uncensored once in the clean version of the soundtrack. In "Love Me" by Obie Trice, Eminem and 50 Cent, the word "goddamn" is left uncensored in 50 Cent's verse.


text: "8 Mile: Music from and Inspired by the Motion Picture" received generally positive reviews from critics. At Metacritic, which assigns a normalized rating out of 100 to reviews from mainstream publications, the album received an average score of 74, based on 20 reviews.


text: Eminem received praise from hip-hop producers and pundits for including authentic, era-appropriate beats in the film, despite the expense associated with clearance relative to original music bearing a similar sound.


text: The album debuted at number one on the "Billboard" 200 with over 702,000 copies sold in the 1st week and 510,000 copies sold in the 2nd week also finishing the year as the 5th best-selling album of 2002 with US sales of over 3.4 million. As of July 2013, it has sold 4,922,000 copies in the U.S. As of January 2016, the soundtrack has sold 11 million copies worldwide.


text: It debuted at number one on the Canadian Albums Chart with sales of 45,000 copies. It also reached number one on the UK Compilations Chart Australian ARIAnet Albums Chart.


title: Székesfehérvár

text: Székesfehérvár (; ), known colloquially as Fehérvár ("white castle"), is a city in central Hungary, and the country's ninth-largest city. It is the regional capital of Central Transdanubia, and the centre of Fejér County and Székesfehérvár District. The area is an important rail and road junction between Lake Balaton and Lake Velence.


title: Székesfehérvár

text: Székesfehérvár, a royal residence ("székhely"), as capital of the Kingdom of Hungary, held a central role in the Middle Ages. As required by the Doctrine of the Holy Crown, the first kings of Hungary were crowned and buried here. Significant trade routes led to the Balkans and Italy, and to Buda and Vienna. Historically the city has come under Ottoman and Habsburg control, and was known in many languages by translations of "white castle" – , , , , , .


title: Székesfehérvár

text: The place has been inhabited since the 5th century BC. In Roman times the settlements were called "Gorsium" and "Herculia". After the Migration Period Fejér County was the part of the Avar Khaganate, while the Slavic and Great Moravian presence is disputed. (There is no source for the name of the place before the late 10th century.) In the Middle Ages its Latin name was "Alba Regalis"/"Alba Regia". The town was an important traffic junction between Lake Balaton and Lake Velence, several trade routes led from here to the Balkans and Italy, and to Buda and Vienna. (Today, the town is a junction of seven railroad lines.)


title: Székesfehérvár

text: Grand Prince Géza of the Árpád dynasty was the nominal overlord of all seven Magyar tribes but in reality ruled only part of the united territory. He aimed to integrate Hungary into Christian Western Europe by rebuilding the state according to the Western political and social models. Géza founded the Hungarian town in 972 on four moorland islands between the Gaja stream and its tributary, the Sárvíz, one of the most important Hungarian tributaries of the Danube. He also had a small stone castle built. Székesfehérvár was first mentioned in a document by the Bishopric of Veszprém, 1009, as "Alba Civitas".


title: Székesfehérvár

text: Stephen I of Hungary granted town rights to the settlement, surrounded the town with a plank wall, and founded a school and a monastery. Under his rule the construction of the Romanesque Székesfehérvár Basilica began (it was built between 1003 and 1038). The settlement had about 3,500 inhabitants at this time and was the royal seat for hundreds of years. 43 kings were crowned in Székesfehérvár (the last one in 1526) and 15 kings were buried here (the last one in 1540).


title: Székesfehérvár

text: In the 12th century, the town prospered, churches, monasteries, and houses were built. It was an important station on the pilgrim route to the Holy Land. András II issued the Golden Bull here in 1222. The Bull included the rights of nobles and the duties of the king, and the Constitution of Hungary was based on it until 1848. It is often compared to England's Magna Carta.


title: Székesfehérvár

text: During the Mongol Invasion of Hungary (1241–1242), the invaders could not get close to the castle: Kadan ruled Mongol warriors could not get through the surrounding marshes because of flooding caused by melting snow. In the 13th–15th centuries, the town prospered, and several palaces were built. In the 14th century, Székesfehérvár was surrounded by city walls.


title: Székesfehérvár

text: After the death of King Mátyás (1490), the German army of 20,000 men of Maximilian invaded Hungary. They advanced into the heart of Hungary and captured the city of Székesfehérvár, which he sacked, as well as the tomb of King Mátyás, which was kept there. His Landsknechts were still unsatisfied with the plunder and refused to go for taking Buda. He returned to the Empire in late December and the Hungarian troops liberated Székesfehérvár in the next year.


title: Székesfehérvár

text: The Ottomans conquered the city after a long siege in 1543 and only after a sally ended in most of the defenders including the commander, György Varkoch, being locked out by wealthy citizens fearing they might incur the wrath of the Ottomans by a lengthy siege.


title: Székesfehérvár

text: Except for a short period in 1601 when Székesfehérvár was reconquered by an army led by Lawrence of Brindisi, the city remained under Ottoman administration for 145 years, until 1688, with the Ottomans being preoccupied with the Morean War. They named the city "Belgrade" ("white city", from Serbian Beograd) and built mosques. In the 16th–17th centuries it looked like a Muslim city. Most of the original population fled. It was a sanjak centre in Budin Province as "İstolni Belgrad" during Ottoman rule.


title: Székesfehérvár

text: The city began to prosper again only in the 18th century. It had a mixed population: Hungarians, Germans, Serbs, and Moravians.


title: Székesfehérvár

text: By 1702, the cathedral of Nagyboldogasszony was blown up, thus destroying the largest cathedral in Hungary at that time, and the coronation temple. By the Doctrine of the Holy Crown, all kings of Hungary were obliged to be crowned in this cathedral, and to take part in coronation ceremony in the surroundings of the cathedral. The coronations after that time were held in Pozsony (now Bratislava).


title: Székesfehérvár

text: In 1703, Székesfehérvár regained the status of a free royal town. In the middle of the century, several new buildings were erected (Franciscan church and monastery, Jesuit churches, public buildings, Baroque palaces). Maria Theresa made the city an episcopal seat in 1777.


title: Székesfehérvár

text: By the early 19th century, the German population was assimilated. On 15 March 1848, the citizens joined the revolution. After the revolution and war for independence, Székesfehérvár lost its importance and became a mainly agricultural city. In 1909 "The Times Engineering Contract List" noted a bridge construction contract valued at £12,000 to be overseen by the Chief Magistrate.


title: Székesfehérvár

text: New prosperity arrived between the two world wars, when several new factories were opened. In 1922 a radio station was established. It used two masts insulated against ground, each with a height of 152 metres. The last mast of the station was demolished in 2009.


title: Székesfehérvár

text: In 1944, after the occupation of Hungary by Nazi Germany, the city's Jewish population was confined to a ghetto and was eventually deported to the Auschwitz death camp, together with further 3,000 Jews from the area. The pre-war Jewish population consisted of Neolog (Reform) and Orthodox communities with their respective synagogues, and some of its members were active Zionists.


title: Székesfehérvár

text: In December 1944, Fehérvár came under Russian artillery fire, and stiff fighting broke out as the Red Army advanced on the city. The Germans had chosen to concentrate their forces to protect the 15 mile gap between Fehérvár and Lake Balaton. Whereas most of the gap consisted of marsh and difficult ground, Fehérvár was the node for eight highways and six railways. Despite the heavy German defences, a Russian flying column broke through and occupied the city on 23 December; the Germans were able to push them out on 22 January 1945. In March 1945, the area was the battleground for the last major German offensive of World War II; but following its failure Marshal Tolbukhin broke through the German lines once more and recaptured the city on 22 March. A Soviet airfield was established at nearby Szabadbattyán.


title: Székesfehérvár

text: After World War II, the city was subject to industrialization, like many other cities and towns in the country. The most important factories were the Ikarus bus factory, the Videoton radio and TV factory, and the Könnyűfémmű (colloquially Köfém) aluminium processing plant, since acquired by Alcoa. By the 1970s, Székesfehérvár had swelled to more than 100,000 inhabitants (in 1945 it had only about 35,000). Several housing estates were built, but the city centre preserved its Baroque atmosphere. The most important Baroque buildings are the cathedral, the episcopal palace and the city hall.


title: Székesfehérvár

text: In the past few decades, archaeologists have excavated medieval ruins (that of the Romanesque basilica and the mausoleum of St. Stephen of Hungary); they can now be visited.


title: Székesfehérvár

text: At the end of the Socialist regime, all the important factories were on the verge of collapse (some eventually folded) and thousands of people lost their jobs. However, the city profited from losing the old and inefficient companies, as an abundance of skilled labour coupled with excellent traffic connections and existing infrastructure attracted numerous foreign firms seeking to invest in Hungary. Székesfehérvár became one of the prime destinations for multinational companies setting up shop in Hungary (Ford and IBM are some of them), turning the city into a success story of Hungary's transition to a market economy. A few years later Denso, Alcoa, Philips, and Sanmina-SCI Corporation also settled in the city.


title: Székesfehérvár

text: The local Municipal Assembly, elected at the 2019 local government elections, is made up of 21 members (1 Mayor, 14 Individual constituencies MEPs and 6 Compensation List MEPs) divided into this political parties and alliances:


title: Székesfehérvár

text: Székesfehérvár is an important hub for the Hungarian railway system ("MÁV"). Trains depart to the Northern Coast of Lake Balaton and towards the capital. The city is also reachable by regional buses from other major national destinations. There are numerous local buslines operating 7 days a week, operated by the company that also operates the regional buses in the region, KNYKK Zrt. ("Közép-Nyugat Magyarországi Közlekedési Központ)."


title: Székesfehérvár

text: Alba Regia Sportcsarnok is an indoor stadium in the city. It hosts a number of sport clubs from amateur to professional level, with 2017 Hungarian basketball championship winner Alba Fehérvár being its most notable tenant.


title: NXT UK Women's Championship

text: The NXT UK Women's Championship was a women's professional wrestling championship that was created and promoted by the American promotion WWE. It was defended on the NXT UK brand division, a sister brand of WWE's developmental territory NXT based in the United Kingdom. Established on 18 June 2018, the inaugural champion was Rhea Ripley. On 4 September 2022 at Worlds Collide, the title was unified into the NXT Women's Championship, officially retiring the title in the process, with Meiko Satomura recognized as the final champion.


title: NXT UK Women's Championship

text: In a press conference at The O2 Arena on 15 December 2016, the American professional wrestling promotion WWE announced plans to establish a United Kingdom-based brand on which professional wrestlers from the country would compete. A men's title, the WWE United Kingdom Championship (later renamed to NXT United Kingdom Championship), was established that same day, however, it was not until mid-2018 when NXT UK was formally established as the United Kingdom-based brand, and sister brand of the American-based NXT. On 18 June during the first night of the 2018 United Kingdom Championship Tournament, the NXT UK Women's Championship, along with the NXT UK Tag Team Championship, was announced for the NXT UK brand. Afterwards, a two-day eight-woman single-elimination tournament was scheduled to crown the inaugural champion during the first tapings of "NXT UK". During the 26 August tapings, Rhea Ripley defeated Toni Storm in the tournament final to become the inaugural NXT UK Women's Champion (aired 28 November).


title: NXT UK Women's Championship

text: In January 2020, WWE briefly began referring to the women's championship as the "NXT UK Championship" to bring it on an equal level as the men's title, though reverted to calling it the NXT UK Women's Championship soon after; it remained as NXT UK Women's Championship on the official title history during this time.


title: NXT UK Women's Championship

text: In August 2022, WWE announced that the NXT UK brand would go on hiatus and would relaunch as NXT Europe in 2023. As such, NXT UK's championships were unified into their respective NXT championship counterparts. Subsequently, the NXT UK Women's Championship was retired on 4 September 2022 at Worlds Collide. At the event, Mandy Rose defeated Blair Davenport and reigning NXT UK Women's Champion Meiko Satomura in a triple threat match to unify the NXT UK Women's Championship into Rose's NXT Women's Championship, with Satomura recognized as the final NXT UK Women's Champion. Rose went forward as the unified NXT Women's Champion.


title: NXT UK Women's Championship

text: The championship belt was revealed during the 25 August 2018 tapings of "NXT UK". It was nearly identical to its male counterpart, the NXT United Kingdom Championship. Like the United Kingdom Championship, the center plate was modeled after the United Kingdom's royal coat of arms, featuring a lion and a horse (instead of the traditional unicorn) on either side of the arms, while at the center of the plate featured a shield with the NXT UK logo; atop the shield were the crown jewels. Above the arms was a banner that read "United Kingdom", directly below the shield was a banner that read "Women's", and below that was a banner that read "Champion". Gold divider bars separated the center plate from the belt's two side plates. In what became a prominent feature for WWE's championship belts, the two side plates featured removable center sections that could be replaced with the reigning champion's logo; the default side plates featured the WWE logo on a red globe. Like the Raw and SmackDown Women's Championship belts, the strap was white and smaller than the men's.


title: NXT UK Women's Championship

text: Over the championship's four-year history, there were four reigns between four champions. Rhea Ripley was the inaugural champion and was also the youngest champion when she won the title at the age of 21, while Meiko Satomura was the oldest at 41 and is recognized as the final champion. Kay Lee Ray had the longest reign, which WWE recognizes as lasting 649 days (beginning 31 August 2019 and ending 10 June 2021); the actual length of her reign is undeterminable as the real date she lost the title is unknown. Ripley had the shortest reign at 139 days (44 days as recognized by WWE due to tape delay).


title: Duke of Bedford

text: Duke of Bedford (named after Bedford, England) is a title that has been created six times (for five distinct people) in the Peerage of England. The first and second creations came in 1414 and 1433 respectively, in favour of Henry IV's third son, John, who later served as regent of France. He was made Earl of Kendal at the same time and was made Earl of Richmond later the same year. The titles became extinct on his death in 1435. The third creation came in 1470 in favour of George Neville, nephew of Warwick the Kingmaker. He was deprived of the title by Act of Parliament in 1478. The fourth creation came in 1478 in favour of George, the third son of Edward IV. He died the following year at the age of two. The fifth creation came in 1485 in favour of Jasper Tudor, half-brother of Henry VI and uncle of Henry VII. He had already been created Earl of Pembroke in 1452. However, as he was a Lancastrian, his title was forfeited between 1461 and 1485 during the predominance of the House of York. He regained the earldom in 1485 when his nephew Henry VII came to the throne and was elevated to the dukedom the same year. He had no legitimate children and the titles became extinct on his death in 1495.


title: Duke of Bedford

text: The Russell family currently holds the titles of Earl and Duke of Bedford. John Russell, a close adviser of Henry VIII and Edward VI, was granted the title of Earl of Bedford in 1551, and his descendant William, 5th Earl, was created Duke in 1694, following the Glorious Revolution.


title: Duke of Bedford

text: The subsidiary titles of the Duke of Bedford, all in the Peerage of England, are Marquess of Tavistock (created 1694), Earl of Bedford (1550), Baron Russell, of Cheneys (1539), Baron Russell of Thornhaugh in the County of Northampton (1603), and Baron Howland, of Streatham in the County of Surrey (1695) (and possibly the Barony of Bedford, which was merged into it in 1138, 1366 or 1414). The courtesy title of the Duke of Bedford's eldest son and heir is "Marquess of Tavistock".


title: Duke of Bedford

text: Every Duke from the 5th Duke onwards is descended from Charles II of England. The family seat is Woburn Abbey, Bedfordshire. The private mausoleum and chapel of the Russell Family and the Dukes of Bedford is at St. Michael's Church in Chenies, Buckinghamshire (photo). The family owns The Bedford Estate in central London.


title: Duke of Bedford

text: The heir apparent is the present holder's only son Henry Robin Charles Russell, Marquess of Tavistock (b. 2005).


title: Howard Baker

text: Howard Henry Baker Jr. (November 15, 1925 June 26, 2014) was an American politician and diplomat who served as a United States Senator from Tennessee from 1967 to 1985. During his tenure, he rose to the rank of Senate Minority Leader and then Senate Majority Leader. A member of the Republican Party, Baker was the first Republican to be elected to the US Senate in Tennessee since the Reconstruction era.


title: Howard Baker

text: Known in Washington, D.C., as the "Great Conciliator", Baker was often regarded as one of the most successful senators in terms of brokering compromises, enacting legislation, and maintaining civility. For example, he had a lead role in the fashioning and passing of the Clean Air Act of 1970 with Democratic senator Edmund Muskie. A moderate conservative, he was also respected by his Democratic colleagues.


title: Howard Baker

text: Baker sought the Republican presidential nomination in 1980 but dropped out after the first set of primaries. From 1987 to 1988, he served as White House Chief of Staff for President Ronald Reagan. From 2001 to 2005, he was the United States Ambassador to Japan.


title: Howard Baker

text: His father served as a Republican member of the US House of Representatives from 1951 to 1964, representing Tennessee's Second District. Baker attended The McCallie School in Chattanooga, and after graduating, he attended Tulane University in New Orleans. Baker was an alumnus of the Alpha Sigma Chapter of the Pi Kappa Phi fraternity. During World War II, he trained at a U.S. Navy facility on the campus of the University of the South in Sewanee, Tennessee, in the V-12 Navy College Training Program. He served as a lieutenant in the United States Navy and graduated from the University of Tennessee College of Law in 1949. That year, he was admitted to the Tennessee bar and began his law practice.


title: Howard Baker

text: Baker began his political career in 1964, when he lost to the liberal Democrat Ross Bass in a US Senate election to fill the unexpired term of the late Senator Estes Kefauver. However, Baker only lost by 4.7 percentage points, the closest that a Republican had come to being popularly elected to the Senate from Tennessee.


title: Howard Baker

text: In the 1966 United States Senate election in Tennessee, Bass lost the Democratic primary to a former Governor of Tennessee, Frank G. Clement, and Baker handily won his Republican primary race against Kenneth Roberts, 112,617 (75.7 percent) to 36,043 (24.2 percent). Baker won the general election, capitalizing on Clement's failure to energize the Democratic base, especially organized labor. He won by a somewhat larger-than-expected margin of 55.7 percent to Clement's 44.2 percent. Baker thus became the first Republican senator from Tennessee since Reconstruction and the first Republican to be popularly elected to the Senate from Tennessee. Baker voted in favor of the Civil Rights Act of 1968 and the confirmation of Thurgood Marshall to the U.S. Supreme Court.


title: Howard Baker

text: Baker was re-elected in 1972 and again in 1978 and served from January 3, 1967, to January 3, 1985. In 1969, he was already a candidate for the Minority Leadership position that opened up with the death of his father-in-law, Everett Dirksen, but Baker was defeated 24–19 by Hugh Scott. At the beginning of the next Congress, in 1971, Baker ran again, losing again to Scott, 24–20.


title: Howard Baker

text: When Scott retired, Baker was elected as leader of the Senate Republicans in 1977 by his Republican colleagues, defeating Robert Griffin, 19–18. Baker led the Senate GOP for the last eight years of his tenure, serving two terms as Senate Minority Leader from 1977 to 1981, and two terms as Senate Majority Leader from 1981 to 1985, a role he transitioned to after the Republicans gained the majority in the Senate in the 1980 elections.


title: Howard Baker

text: Baker did not seek further re-election and concluded his Senate career in 1985. He was succeeded by Democratic Representative and future Vice President Al Gore.


title: Howard Baker

text: President Richard Nixon asked Baker in 1971 to fill one of the two empty seats on the US Supreme Court. When Baker took too long to decide whether he wanted the appointment, Nixon changed his mind and nominated William Rehnquist instead.


title: Howard Baker

text: In 1973 and 1974, Baker was the influential ranking minority member of the Senate Watergate Committee, chaired by Senator Sam Ervin, which investigated the Watergate scandal. Baker famously asked aloud, "What did the President know and when did he know it?" The question is sometimes attributed to being given to him by his counsel and former campaign manager, future US Senator Fred Thompson.


title: Howard Baker

text: John Dean, former White House counsel to Nixon, revealed to Senate Watergate chief counsel Samuel Dash that Baker had "secret dealings" with the White House during the congressional investigation. Although Baker, as a US senator, would be a juror in any future impeachment trial, Baker was recorded, on February 22, 1973, promising Nixon, "I'm your friend. I'm going to see that your interests are protected."


title: Howard Baker

text: Watergate reporter Bob Woodward wrote that then "both the majority Democrats and minority Republicans agreed to share all information." Ultimately, one such document shared by Nixon lawyer Fred Buzhardt inadvertently suggested the presence of Nixon's secret taping system.


title: Howard Baker

text: Baker was frequently mentioned by insiders as a possible nominee for Vice President of the United States on a ticket headed by incumbent President Gerald Ford in 1976. According to many sources, Baker was a frontrunner until he disclosed that his wife, Joy, was a recovered alcoholic. Ford, whose own wife, Betty, was an alcoholic (albeit undisclosed at the time), chose Kansas Senator Bob Dole.


title: Howard Baker

text: Baker ran for U.S. president in 1980, dropping out of the race for the Republican nomination after losing the Iowa caucuses to George H. W. Bush and the New Hampshire primary to Ronald Reagan even though a Gallup poll had him in second place in the presidential race at 18%, behind Reagan at 41% as late as November 1979. Baker's support of the 1978 Panama Canal Treaties was overwhelmingly unpopular, especially among Republicans, and it was a factor in Reagan's choosing Bush instead as his running mate. Ted Stevens served as Acting Minority Leader during Baker's primary campaign.


title: Howard Baker

text: In October 1983, Baker voted in favor of the bill establishing Martin Luther King Jr. Day as a federal holiday.


title: Howard Baker

text: As a testament to Baker's skill as a negotiator and an honest and amiable broker, Reagan tapped him to serve as Chief of Staff during part of Reagan's second term (1987–1988). Many saw that as a move by Reagan to mend relations with the Senate, which had deteriorated somewhat under the previous chief of staff, Donald Regan. In accepting the appointment, Baker chose to skip another bid for the White House in 1988.


title: Howard Baker

text: In 2003, the Howard H. Baker, Jr. Center for Public Policy was set up at the University of Tennessee to honor him. Vice President Dick Cheney gave a speech at the 2005 ground-breaking ceremony for the center's new building. Upon the building's completion in 2008, US Supreme Court Justice Sandra Day O'Connor assisted in the facility's dedication.


title: Howard Baker

text: In 2007, Baker joined fellow former Senate Majority Leaders Bob Dole, Tom Daschle, and George Mitchell to found the Bipartisan Policy Center, a non-profit think tank that works to develop policies suitable for bipartisan support. He was an advisory board member for the Partnership for a Secure America, a not-for-profit organization dedicated to recreating the bipartisan center in American national security and foreign policy. From 2005 to 2011, Baker was a member of the board of directors of the International Foundation for Electoral Systems, a nonprofit that provides international election support.


title: Howard Baker

text: From 2005 to his death, Baker served as Senior Counsel to the law firm of his father and his grandfather, Baker, Donelson, Bearman, Caldwell & Berkowitz.


title: Howard Baker

text: Baker was an accomplished lifelong photographer. His photographs have often been exhibited and were published in "National Geographic", "Life", and in the books "Howard Baker's Washington" (1982), "Big South Fork Country" (1993), and "Scott's Gulf: The Bridgestone/Firestone Centennial Wilderness" (2000). In 1993, he received the International Award of the American Society of Photographers, and in 1994, he was elected into the Hall of Fame of the Photo Marketing Association.


title: Howard Baker

text: On June 26, 2014, Baker died at the age of 88 from complications of a stroke that he had suffered a week earlier. He was in his native Huntsville, Tennessee, with his wife, Nancy, by his side.


title: Howard Baker

text: Baker, a Presbyterian, was married twice. His first wife, Joy Dirksen, with whom he had two daughters, was herself the daughter of former Senate Minority Leader Everett M. Dirksen (R). After his first wife died of cancer in 1993, he remarried in 1996 to fellow U.S. Senator Nancy Landon Kassebaum, who also happened to be the daughter of a Republican politician, Kansas Governor Alfred M. Landon, the unsuccessful Republican nominee for President in 1936 against Democratic incumbent Franklin Roosevelt.


title: IBM AIX

text: AIX (Advanced Interactive eXecutive, pronounced , "ay-eye-ex") is a series of proprietary Unix operating systems developed and sold by IBM for several of its computer platforms.


title: IBM AIX

text: Originally released for the IBM RT PC RISC workstation in 1986, AIX has supported a wide variety of hardware platforms, including the IBM RS/6000 series and later Power and PowerPC-based systems, IBM System i, System/370 mainframes, PS/2 personal computers, and the Apple Network Server. It is currently supported on IBM Power Systems alongside IBM i and Linux.


title: IBM AIX

text: AIX is based on UNIX System V with 4.3BSD-compatible extensions. It is certified to the UNIX 03 and UNIX V7 marks of the Single UNIX Specification, beginning with AIX versions 5.3 and 7.2 TL5 respectively. Older versions were previously certified to the UNIX 95 and UNIX 98 marks.


title: IBM AIX

text: AIX was the first operating system to have a journaling file system, and IBM has continuously enhanced the software with features such as processor, disk and network virtualization, dynamic hardware resource allocation (including fractional processor units), and reliability engineering ported from its mainframe designs.


title: IBM AIX

text: Unix started life at AT&T's Bell Labs research center in the early 1970s, running on DEC minicomputers. By 1976, the operating system was in use at various academic institutions, including Princeton, where Tom Lyon and others ported it to the S/370, to run as a guest OS under VM/370. This port would later grow out to become UTS, a mainframe Unix offering by IBM's competitor Amdahl Corporation.


title: IBM AIX

text: IBM's own involvement in Unix can be dated to 1979, when it assisted Bell Labs in doing its own Unix port to the 370 (to be used as a build host for the 5ESS switch's software). In the process, IBM made modifications to the TSS/370 hypervisor to better support Unix.


title: IBM AIX

text: It took until 1985 for IBM to offer its own Unix on the S/370 platform, IX/370, which was developed by Interactive Systems Corporation and intended by IBM to compete with Amdahl UTS. The operating system offered special facilities for interoperating with PC/IX, Interactive/IBM's version of Unix for IBM PC compatible hardware, and was licensed at $10,000 per sixteen concurrent users.


title: IBM AIX

text: AIX Version 1, introduced in 1986 for the IBM RT PC workstation, was based on UNIX System V Releases 1 and 2. In developing AIX, IBM and Interactive Systems Corporation (whom IBM contracted) also incorporated source code from 4.2 and 4.3 BSD UNIX.


title: IBM AIX

text: Among other variants, IBM later produced AIX Version 3 (also known as AIX/6000), based on System V Release 3, for their POWER-based RS/6000 platform. Since 1990, AIX has served as the primary operating system for the RS/6000 series (later renamed "IBM eServer pSeries", then "IBM System p", and now "IBM Power Systems"). AIX Version 4, introduced in 1994, added symmetric multiprocessing with the introduction of the first RS/6000 SMP servers and continued to evolve through the 1990s, culminating with AIX 4.3.3 in 1999. Version 4.1, in a slightly modified form, was also the standard operating system for the Apple Network Server systems sold by Apple Computer to complement the Macintosh line.


title: IBM AIX

text: In the late 1990s, under Project Monterey, IBM and the Santa Cruz Operation planned to integrate AIX and UnixWare into a single 32-bit/64-bit multiplatform UNIX with particular emphasis on running on Intel IA-64 (Itanium) architecture CPUs. A beta test version of AIX 5L for IA-64 systems was released, but according to documents released in the "SCO v. IBM" lawsuit, less than forty licenses for the finished Monterey Unix were ever sold before the project was terminated in 2002. In 2003, the SCO Group alleged that (among other infractions) IBM had misappropriated licensed source code from UNIX System V Release 4 for incorporation into AIX; SCO subsequently withdrew IBM's license to develop and distribute AIX. IBM maintains that their license was irrevocable, and continued to sell and support the product until the litigation was adjudicated.


title: IBM AIX

text: AIX was a component of the 2003 "SCO v. IBM" lawsuit, in which the SCO Group filed a lawsuit against IBM, alleging IBM contributed SCO's intellectual property to the Linux codebase. The SCO Group, who argued they were the rightful owners of the copyrights covering the Unix operating system, attempted to revoke IBM's license to sell or distribute the AIX operating system. In March 2010, a jury returned a verdict finding that Novell, not the SCO Group, owns the rights to Unix.


title: IBM AIX

text: AIX 6 was announced in May 2007, and it ran as an open beta from June 2007 until the general availability (GA) of AIX 6.1 on November 9, 2007. Major new features in AIX 6.1 included full role-based access control, workload partitions (which enable application mobility), enhanced security (Addition of AES encryption type for NFS v3 and v4), and Live Partition Mobility on the POWER6 hardware.


title: IBM AIX

text: AIX 7.1 was announced in April 2010, and an open beta ran until general availability of AIX 7.1 in September 2010. Several new features, including better scalability, enhanced clustering and management capabilities were added. AIX 7.1 includes a new built-in clustering capability called Cluster Aware AIX. AIX is able to organize multiple LPARs through the multipath communications channel to neighboring CPUs, enabling very high-speed communication between processors. This enables multi-terabyte memory address range and page table access to support global petabyte shared memory space for AIX POWER7 clusters so that software developers can program a cluster as if it were a single system, without using message passing (i.e. semaphore-controlled Inter-process Communication). AIX administrators can use this new capability to cluster a pool of AIX nodes. By default, AIX V7.1 pins kernel memory and includes support to allow applications to pin their kernel stack. Pinning kernel memory and the kernel stack for applications with real-time requirements can provide performance improvements by ensuring that the kernel memory and kernel stack for an application is not paged out.


title: IBM AIX

text: AIX 7.2 was announced in October 2015, and released in December 2015. The principal feature of AIX 7.2 is the Live Kernel Update capability, which allows OS fixes to replace the entire AIX kernel with no impact to applications, by live migrating workloads to a temporary surrogate AIX OS partition while the original OS partition is patched. AIX 7.2 was also restructured to remove obsolete components. The networking component, bos.net.tcp.client was repackaged to allow additional installation flexibility. Unlike AIX 7.1, AIX 7.2 is only supported on systems based on POWER7 or later processors.


title: IBM AIX

text: The original AIX (sometimes called AIX/RT) was developed for the IBM RT PC workstation by IBM in conjunction with Interactive Systems Corporation, who had previously ported UNIX System III to the IBM PC for IBM as PC/IX. According to its developers, the AIX source (for this initial version) consisted of one million lines of code. Installation media consisted of eight 1.2M floppy disks. The RT was based on the IBM ROMP microprocessor, the first commercial RISC chip. This was based on a design pioneered at IBM Research (the IBM 801).


title: IBM AIX

text: One of the novel aspects of the RT design was the use of a microkernel, called Virtual Resource Manager (VRM). The keyboard, mouse, display, disk drives and network were all controlled by a microkernel. One could "hotkey" from one operating system to the next using the Alt-Tab key combination. Each OS in turn would get possession of the keyboard, mouse and display. Besides AIX v2, the PICK OS also included this microkernel.


title: IBM AIX

text: Much of the AIX v2 kernel was written in the PL/8 programming language, which proved troublesome during the migration to AIX v3. AIX v2 included full TCP/IP networking, as well as SNA and two networking file systems: NFS, licensed from Sun Microsystems, and Distributed Services (DS). DS had the distinction of being built on top of SNA, and thereby being fully compatible with DS on and on midrange systems running OS/400 through IBM i. For the graphical user interfaces, AIX v2 came with the X10R3 and later the X10R4 and X11 versions of the X Window System from MIT, together with the Athena widget set. Compilers for Fortran and C were available.


title: IBM AIX

text: AIX PS/2 (also known as AIX/386) was developed by Locus Computing Corporation under contract to IBM. AIX PS/2, first released in October 1988, ran on IBM PS/2 personal computers with Intel 386 and compatible processors.


title: IBM AIX

text: The product was announced in September 1988 with a baseline tag price of $595, although some utilities like uucp were included in a separate Extension package priced at $250. nroff and troff for AIX were also sold separately in a Text Formatting System package priced at $200. The TCP/IP stack for AIX PS/2 retailed for another $300. The X Window System package was priced at $195, and featured a graphical environment called the AIXwindows Desktop, based on IXI's X.desktop. The C and FORTRAN compilers each had a price tag of $275. Locus also made available their DOS Merge virtual machine environment for AIX, which could run MS DOS 3.3 applications inside AIX; DOS Merge was sold separately for another $250. IBM also offered a $150 AIX PS/2 DOS Server Program, which provided file server and print server services for client computers running PC DOS 3.3.


title: IBM AIX

text: The last version of PS/2 AIX is 1.3. It was released in 1992 and announced to add support for non-IBM (non-microchannel) computers as well. Support for PS/2 AIX ended in March 1995.


title: IBM AIX

text: In 1988, IBM announced AIX/370, also developed by Locus Computing. AIX/370 was IBM's fourth attempt to offer Unix-like functionality for their mainframe line, specifically the System/370 (the prior versions were a TSS/370-based Unix system developed jointly with AT&T c.1980, a VM/370-based system named VM/IX developed jointly with Interactive Systems Corporation c.1984, and a VM/370-based version of TSS/370 named IX/370 which was upgraded to be compatible with Unix System V). AIX/370 was released in 1990 with functional equivalence to System V Release 2 and 4.3BSD as well as IBM enhancements. With the introduction of the ESA/390 architecture, AIX/370 was replaced by AIX/ESA in 1991, which was based on OSF/1, and also ran on the System/390 platform. This development effort was made partly to allow IBM to compete with Amdahl UTS. Unlike AIX/370, AIX/ESA ran both natively as the host operating system, and as a guest under VM. AIX/ESA, while technically advanced, had little commercial success, partially because UNIX functionality was added as an option to the existing mainframe operating system, MVS, as MVS/ESA SP Version 4 Release 3 OpenEdition in 1994, and continued as an integral part of MVS/ESA SP Version 5, OS/390 and z/OS, with the name eventually changing from "OpenEdition" to "Unix System Services". IBM also provided OpenEdition in VM/ESA Version 2 through z/VM.


title: IBM AIX

text: As part of Project Monterey, IBM released a beta test version of AIX 5L for the IA-64 (Itanium) architecture in 2001, but this never became an official product due to lack of interest.


title: IBM AIX

text: The Apple Network Server (ANS) systems were PowerPC-based systems designed by Apple Computer to have numerous high-end features that standard Apple hardware did not have, including swappable hard drives, redundant power supplies, and external monitoring capability. These systems were more or less based on the Power Macintosh hardware available at the time but were designed to use AIX (versions 4.1.4 or 4.1.5) as their native operating system in a specialized version specific to the ANS called AIX for Apple Network Servers.


title: IBM AIX

text: AIX was only compatible with the Network Servers and was not ported to standard Power Macintosh hardware. It should not be confused with A/UX, Apple's earlier version of Unix for 68k-based Macintoshes.


title: IBM AIX

text: The release of AIX version 3 (sometimes called AIX/6000) coincided with the announcement of the first POWER1-based IBM RS/6000 models in 1990.


title: IBM AIX

text: AIX v3 innovated in several ways on the software side. It was the first operating system to introduce the idea of a journaling file system, JFS, which allowed for fast boot times by avoiding the need to ensure the consistency of the file systems on disks (see fsck) on every reboot. Another innovation was shared libraries which avoid the need for static linking from an application to the libraries it used. The resulting smaller binaries used less of the hardware RAM to run, and used less disk space to install. Besides improving performance, it was a boon to developers: executable binaries could be in the tens of kilobytes instead of a megabyte for an executable statically linked to the C library. AIX v3 also scrapped the microkernel of AIX v2, a contentious move that resulted in v3 containing no PL/8 code and being somewhat more "pure" than v2.


title: IBM AIX

text: The default shell was Bourne shell up to AIX version 3, but was changed to KornShell (ksh88) in version 4 for XPG4 and POSIX compliance.


title: IBM AIX

text: The Common Desktop Environment (CDE) is AIX's default graphical user interface. As part of Linux Affinity and the free AIX Toolbox for Linux Applications (ATLA), open-source KDE Plasma Workspaces and GNOME desktop are also available.


title: IBM AIX

text: SMIT is the System Management Interface Tool for AIX. It allows a user to navigate a menu hierarchy of commands, rather than using the command line. Invocation is typically achieved with the command codice_1. Experienced system administrators make use of the codice_2 function key which generates the command line that SMIT will invoke to complete it.


title: IBM AIX

text: SMIT also generates a log of commands that are performed in the codice_3 file. The codice_3 file automatically records the commands with the command flags and parameters used. The codice_3 file can be used as an executable shell script to rerun system configuration tasks. SMIT also creates the codice_6 file, which contains additional detailed information that can be used by programmers in extending the SMIT system.


title: IBM AIX

text: codice_1 and codice_8 refer to the same program, though codice_8 invokes the text-based version, while codice_1 will invoke an X Window System based interface if possible; however, if codice_1 determines that X Window System capabilities are not present, it will present the text-based version instead of failing. Determination of X Window System capabilities is typically performed by checking for the existence of the codice_12 variable.


title: IBM AIX

text: Object Data Manager (ODM) is a database of system information integrated into AIX, analogous to the registry in Microsoft Windows. A good understanding of the ODM is essential for managing AIX systems.


title: IBM AIX

text: Data managed in ODM is stored and maintained as objects with associated attributes. Interaction with ODM is possible via application programming interface (API) library for programs, and command-line utilities such as "odmshow", "odmget", "odmadd", "odmchange" and "odmdelete" for shell scripts and users. SMIT and its associated AIX commands can also be used to query and modify information in the ODM. ODM is stored on disk using Berkeley DB files.


title: Persona 4 Arena Ultimax

text: Persona 4 Arena Ultimax is a fighting video game co-developed by Atlus and Arc System Works. It was released for arcades in 2013, and for PlayStation 3 and Xbox 360 in 2014 by Atlus in Japan and North America and by Sega in Europe. Versions for Nintendo Switch, PlayStation 4, and Windows were released by Sega in March 2022. A direct sequel to "Persona 4 Arena", the game is a spin-off from the "Persona" series, itself part of the larger "Megami Tensei" franchise. Gameplay follows standard fighting game conventions, with matches between two characters with individual movesets, and special expendable abilities. The storyline is told through visual novel segments.


title: Persona 4 Arena Ultimax

text: "Arena Ultimax" takes place immediately after "Persona 4 Arena"; "Arena" is set two months after the events of "Persona 4", and three years after "Persona 3". After a supernatural red fog covers the town of Inaba, the Investigation Team of "Persona 4" team up with the Shadow Operatives—a group formed by the SEES of "Persona 3"—to uncover the truth behind the P-1 Grand Prix and confront Sho Minazuki, a hostile Persona user.


title: Persona 4 Arena Ultimax

text: "Arena Ultimax" was intended as closure for the story of "Arena", beginning production following the first game's critical and commercial success. The main staff of "Arena" returned for "Arena Ultimax", with the gameplay being refined based on feedback. The release was supported by downloadable content featuring characters and accessories. Selling over 280,000 units worldwide, the game was positively reviewed by critics; many cited the gameplay as superior to its predecessor.


title: Persona 4 Arena Ultimax

text: Like the previous title, "Persona 4 Arena Ultimax" is a fighting game in which characters from both "Persona 3" and "Persona 4" battle each other using a variety of special moves, combos, and Persona abilities. Along with balance adjustments and new moves added to the returning characters, a new S-Hold system allows players to charge up their attacks. Also new to "Ultimax" is the ability to choose "Shadow Types" for many of the game's characters. These Shadow versions differ by being largely skill-based variants of their counterparts, employing a high-risk high-reward play style thanks to reduced normal attack damage and an altered move set with lacking defensive options. They also keep their SP over multiple rounds instead of resetting each time and possess an exclusive Shadow Rampage ability, which replaces the Burst command, giving the characters infinite SP for a limited time.


title: Persona 4 Arena Ultimax

text: The game's story mode, told in a format similar to a visual novel, is now split into two separate campaigns for the "Persona 3" and "Persona 4" casts, as opposed to the individual campaigns for each character seen in its predecessor. Two additional story campaigns are available: one allows players to replay the character campaigns from "Persona 4 Arena", while the other is included with the Tohru Adachi downloadable content and focuses on his actions during the story. Golden Arena is a dungeon-based survival mode, in which players travel through various dungeons to increase the fighter's stats. Players can build Social Links with a navigator character, giving them skills that can aid the player in battle. Online play now features a new lobby area, resembling an arcade, where players can interact with each other.


title: Persona 4 Arena Ultimax

text: Along with all thirteen returning characters from the previous game, eight new characters (five of which were initially exclusive to the console version) are added to the roster, including a brand new character, Sho Minazuki. Furthermore, many (though not all) of these characters feature a Shadow Type. Other characters, such as Fuuka Yamagishi from "Persona 3" and Theodore from "Persona 3 Portable" appear in a non-playable capacity.


title: Persona 4 Arena Ultimax

text: The "Persona 4 Arena" duology are set in the "Persona" universe, revolving around groups of people who harness Personas—physical manifestations of their inner psyche—and use them to fight rogue psyches born from humanity's collective unconsciousness called Shadows. "Arena Ultimax" takes place a few days after the ending of "Persona 4 Arena", which is set three months after the events of "Persona 4" and two years after "Persona 3". A key location in the story is the TV World, a metaphysical realm influenced by the wills of Inaba's population. "Arena Ultimax" begins when Inaba, the central location for both "Persona 4" and "Arena", is covered by a red fog. The previous game's otherworldly fighting tournament returns as the "P-1 Climax", seeping into the real world and warping Inaba with replicas of locations from "Persona 3".


title: Persona 4 Arena Ultimax

text: The story is split into two parallel narratives following the Investigation Team of "Persona 4" (Yu Narukami, Yosuke Hanamura, Chie Satonaka, Yukiko Amagi, Kanji Tatsumi, Rise Kujikawa, Naoto Shirogane, and the friendly Shadow-like entity Teddie), a group of friends who investigated a serial murder case involving the TV World; and the Shadow Operatives (Aigis, Mitsuru Kirijo, Akihiko Sanada, Yukari Takeba, Junpei Iori, Fuuka Yamagishi, Ken Amada and the dog Koromaru), a covert group formed by former SEES members from "Persona 3" to fight Shadows. The main protagonists of "Persona 4 Arena" are "Persona 4" protagonist Narukami—a character blessed with the "Wild Card" power to summon multiple Personas; and Labrys, an Anti-Shadow weapon allied with the Shadow Operatives introduced in "Arena". The main antagonists are Sho Minazuki, an unstable Persona user with a split personality; and Hi-no-Kagutsuchi, a being generated by the human Collective Unconscious and its latent despair. Additional characters in the narrative and DLC gameplay are Tohru Adachi, a central antagonist in "Persona 4"; Marie, a character introduced in "Persona 4 Golden"; and Margaret and Elizabeth, attendants from the metaphysical Velvet Room.


title: Persona 4 Arena Ultimax

text: At midnight the day after the P-1 Grand Prix concludes, the TV in Yu's room suddenly starts airing the Midnight Channel, showing a promotion for the P-1 Climax and an image of Mitsuru, Aigis, Akihiko, and Fuuka crucified, with General Teddie announcing the world's end within one hour. A black out then occurs throughout Inaba and the whole town is surrounded by red fog. The Investigation Team starts facing Shadows that steal fragments of their enemies' Personas upon their defeat.


title: Persona 4 Arena Ultimax

text: Yu, Yosuke, and Chie reach Yasogami and are approached by Sho, who announces himself as the culprit behind the P-1 Grand Prix and P-1 Climax. Tohru Adachi, whom they had believed to be imprisoned, also appears and give cryptic clues regarding Sho's intentions to them. At Tartarus, they once again encounter Sho and defeat him, but are overpowered by his other self, Minazuki. Minazuki reveals he has gathered enough Persona fragments to summon a malevolent entity that will destroy the world. Now reunited with their teammates and the Shadow Operatives, they pursue him through Tartarus, but are assaulted by a huge group of Shadows, forcing the group to let Yu confront Sho alone. At the top of the tower, Yu stops Sho from killing Adachi and fights Minazuki, emerging victorious. General Teddie, revealing himself to be Hi-no-Kagutsuchi, possesses Sho's body and fights Yu. With Adachi's help, they are able to kill Kagutsuchi. As Tartarus collapses, Elizabeth transports everyone to the TV World where Yu reunites with his friends who all set up to find a new path in their futures.


title: Persona 4 Arena Ultimax

text: Following this scenario, the game shows how Mitsuru, Aigis, Akihiko, and Fuuka are captured by Sho. In response, the reserve members of the Shadow Operatives as well as Labrys all head to Inaba via helicopter (save for Junpei, who ends up at Inaba by accident). After separately meeting the members of Investigation Team and rescuing their captured friends, they encounter Sho, the culprit behind Labrys' hijacking, who goads them into fighting Shadows in order to gather their Persona fragments. It is also revealed that Sho used to be one of Ikutsuki's test subjects, living in isolation and growing to resent the entire world after Ikutsuki abandoned him. During one of the experiments to make him a Persona user like Strega by using a Plume of Dusk, similar to Aigis and Labrys, another personality was born within Sho, which he refers to as Minazuki. Minazuki claims to have been born to grant Sho's wish to create a world without bonds. Labrys reaches the top of the tower and fights Sho. After defeating Sho, Hi-no-Kagutsuchi proceeds to possesses Sho's body. Labrys is able to emerge victorious against Kagutsuchi. As Tartarus collapses, Margaret transports everyone into the TV World. Sho, who is also transported, asks if she really did defeat Kagutsuchi, which she confirms, and Sho acknowledges her strength. Labrys then tries to convince Sho to join the Shadow Operatives.


title: Persona 4 Arena Ultimax

text: After both scenarios are completed, a new chapter is unlocked, which follows the aftermath of Yu and Adachi defeating Kagutsuchi. As both Margaret and Elizabeth appear to witness the event, Sho is revealed to be alive but unable to sense Minazuki within him. He then reveals that after waking up from his coma, he discovered that Ikutsuki paid his medical expenses, leaving him with a large inheritance. Feeling lost due to only knowing how to fight, Sho eventually settled in Inaba, where he observed the Investigation Team solving the serial kidnapping/murder case in secret, believing that they might be able to understand him, only to give up. It was then that he was confronted by Hi-no-Kagutsuchi, who asked him if he wanted to destroy the world, and he agreed. Realizing Sho's only way to form bonds is by fighting, Yu starts a rivalry with him and both fight, revealing that Minazuki's persona has been passed down to Sho. After Sho's friendly departure, Margaret safely transports everyone out of the tower at Yu's request, and come to terms with Elizabeth's departure from the Velvet Room after trying to convince her to return. In the aftermath, the Investigation Team and the Shadow Operatives reunite at Junes, where they all bond together and celebrate their victory, while Yu and Labrys express their belief in Sho finding bonds of his own.


title: Persona 4 Arena Ultimax

text: Adachi continues to undergo police questioning regarding his methods for the two murders in Inaba, but his interrogators still do not believe his account of his crimes. However, Sho brainwashes one of the interrogators into assaulting Adachi and forcing him back into the TV World. There, Sho coerces Adachi into aiding him in his plan to destroy the world. Adachi outwardly agrees, but decides to sabotage him in order to honor his promise to the Investigation Team to follow society's rules, and subtly manipulates the Investigation Team towards defeating Sho. When the Persona fragments are collected, Adachi makes his move, betraying Sho. However, Minazuki brutally beats Adachi. Adachi is rescued by Yu, and he destroys Hi-no-Kagutsuchi with Yu's aid. Adachi then returns to his prison cell and is visited by his old friend Dojima the next day.


title: Persona 4 Arena Ultimax

text: "Persona 4 Arena Ultimax" was co-developed by "Persona" series creator Atlus, and fighting game developer Arc System Works, featuring several returning staff. These included "Persona" series producer Katsura Hashino and director Kazuhisa Wada from Atlus; and producers Takumi Iguchiya and Toshimichi Mori, and director Seiji Fukumoto from the "BlazBlue" production unit. The new character designs were designed by "Persona" series artist Shigenori Soejima. The anime cutscenes were created by Capsule, a company previously known for their CGI support work on series including "Space Dandy". While the original "Arena" was always planned as a standalone work, Wada wanted to leave the narrative open to a sequel that could both expand the narrative and improve on its gameplay mechanics. The success of "Arena" prompted a sequel to begin production.