Comparative Survey on Association Rule Mining Algorithms
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paper_title: Efficient search for association rules

paper_content:

This paper argues that for some applications direct search for association rules can be more e cient than the tw o stage process of the Apriori algorithm which rst nds large itemsets whic hare then used to iden tify associations. In particular, it is argued, Apriori can impose large computational overheads when the number of frequen titemsets is very large. This will often be the case when association rule analysis is performed on domains other than basket analysis or when it is performed for basket analysis with basket information augmented b y other customer information. An algorithm is presented that is computationally e cient for association rule analyses during which the n umber of rules to be found can be constrained and all data can be maintained in memory.
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paper_title: Algorithms for association rule mining — a general survey and comparison

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ABSTRACT Today there are several eAE ient algorithms that ope with the popular and omputationally expensive task of asso iation rule mining. A tually, these algorithms are more or less des ribed on their own. In this paper we explain the fundamentals of asso iation rule mining and moreover derive a general framework. Based on this we des ribe today's approa hes in ontext by pointing out ommon aspe ts and di eren es. After that we thoroughly investigate their strengths and weaknesses and arry out several runtime experiments. It turns out that the runtime behavior of the algorithms is mu h more similar as to be expe ted.
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paper_title: Research on Association Rule Mining

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Association Rule Mining is one of the important areas of research, receiving increasing attention. It is an essential part of Knowledge Discovery in Databases (KDD). The scope of Association Rule Mining and KDD is very broad. Over the last fifteen years it has been developed at a dynamic rate. Although it has been emerged as a new technology but Association Rule Mining is still in a stage of exploration and development. In this paper we present a survey of research work carried by different researchers since its beginning. Of course, a single article cannot be a complete review of all the research work, yet we hope that it will provide a guideline for the researcher in interesting research directions that have yet to be explored.
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paper_title: Performance comparison of apriori and FP-growth algorithms in generating association rules

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In this article we present a performance comparison between Apriori and FP-Growth algorithms in generating association rules. The two algorithms are implemented in Rapid Miner and the result obtain from the data processing are analyzed in SPSS. The database used in the development of processes contains a series of transactions belonging to an online shop.
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Title: Comparative Survey on Association Rule Mining Algorithms

Section 1: INTRODUCTION
Description 1: Introduce the importance of data mining for business analysis, define association rule learning, and highlight the need for effective algorithms.

Section 2: LITERATURE REVIEW
Description 2: Summarize previous research and studies on association rule mining techniques, highlighting key findings and contributions from various researchers.

Section 3: INTRODUCTION TO ALGORITHMS
Description 3: Provide an overview of various association rule mining algorithms, discussing their methodologies and unique attributes.

Section 3.1: AIS ALGORITHM
Description 3.1: Discuss the AIS algorithm, its approach to detecting frequent item sets, and its drawbacks such as the generation of too many candidate item sets.

Section 3.2: SETM ALGORITHM
Description 3.2: Explain the SETM algorithm, its basis in SQL operations, and its differences from AIS, including the separation of candidate generation and counting.

Section 3.3: APRIORI ALGORITHM
Description 3.3: Describe the Apriori algorithm, its use of breadth-first search, and its method of generating and testing fewer candidate item sets.

Section 3.4: APRIORI TID ALGORITHM
Description 3.4: Outline the AprioriTID algorithm, its differences from Apriori in terms of counting support without referring to the database, and performance benefits.

Section 3.5: APRIORI HYBRID ALGORITHM
Description 3.5: Discuss the Apriori Hybrid algorithm, which combines Apriori and AprioriTID's advantages by switching methods during the process.

Section 3.6: ECLAT ALGORITHM
Description 3.6: Explore the Eclat algorithm, emphasizing the bit matrix representation and depth-first search in a prefix tree for finding item sets.

Section 3.7: RECURSIVE ELIMINATION ALGORITHM
Description 3.7: Explain the Recursive Elimination algorithm, which involves step-by-step item elimination and recursive processing.

Section 3.8: FP GROWTH ALGORITHM
Description 3.8: Introduce the FP-Growth algorithm, describing how it overcomes Apriori's limitations by using an FP-tree structure for efficient frequent pattern mining.

Section 4: CONCLUSION
Description 4: Summarize the key findings from the comparison of various algorithms and conclude with insights on the most effective methods for different scenarios.