A Review: Data Mining over Multi-Relations
---
paper_title: Data Mining Concepts and Techniques

paper_content:

Understand the need for analyses of large, complex, information-rich data sets. Identify the goals and primary tasks of the data-mining process. Describe the roots of data-mining technology. Recognize the iterative character of a data-mining process and specify its basic steps. Explain the influence of data quality on a data-mining process. Establish the relation between data warehousing and data mining. Data mining is an iterative process within which progress is defined by discovery, through either automatic or manual methods. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an "interesting" outcome. Data mining is the search for new, valuable, and nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers. In practice, the two primary goals of data mining tend to be prediction and description. Prediction involves using some variables or fields in the data set to predict unknown or future values of other variables of interest. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans. Therefore, it is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining, which produces new, nontrivial information based on the available data set.
---
paper_title: Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

paper_content:

Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns. ::: ::: In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods.
---
paper_title: Survey of Classification Techniques in Data Mining

paper_content:

Classification is a data mining technique based on machine learning which is used to classify each item in a set of data into a set of predefined classes or groups. Classification methods make use of mathematical and statistical techniques such as decision trees, linear programming, neural network and support vector machines. Classification is process of generalizing the data according to different instances. Several major kinds of classification algorithms including C4.5, k-nearest neighbour classifier, Naive Bayes, SVM, and IB3.This paper provide an inclusive survey of different classification algorithms and their advantages and disadvantages.
---
paper_title: A Brief Review on Frequent Pattern Mining

paper_content:

frequent pattern mining plays an essential role in association rule mining, which has been a focused theme in data mining research for over the past 15 years. Since the pioneering work of Agrawal, abundant literature has been dedicated to this research. In this article, we provide a brief overview of the current status of frequent pattern mining and discuss a few promising research directions.
---
paper_title: Data Mining Concepts and Techniques

paper_content:

Understand the need for analyses of large, complex, information-rich data sets. Identify the goals and primary tasks of the data-mining process. Describe the roots of data-mining technology. Recognize the iterative character of a data-mining process and specify its basic steps. Explain the influence of data quality on a data-mining process. Establish the relation between data warehousing and data mining. Data mining is an iterative process within which progress is defined by discovery, through either automatic or manual methods. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an "interesting" outcome. Data mining is the search for new, valuable, and nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers. In practice, the two primary goals of data mining tend to be prediction and description. Prediction involves using some variables or fields in the data set to predict unknown or future values of other variables of interest. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans. Therefore, it is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining, which produces new, nontrivial information based on the available data set.
---
paper_title: Multi Relational Mining in Network Intrusion Detection

paper_content:

The network intrusion detection (NIDS) is faced with the question to detect many kinds of intrusion. In order to detect the complex attack, network intrusion detection system need to analysis massive data captured form different network safety equipments. So a new multi relational mining algorithm MRA2 is proposed. MRA2 depend on the association rules mining technology and the probability function dependency method which is proposed through extending the theory of function dependency. MRA2 is able to synthesize the various datalog resources to detect intrusion effectively and reappear to the complex network attack scenario.
---
paper_title: A Brief Review on Frequent Pattern Mining

paper_content:

frequent pattern mining plays an essential role in association rule mining, which has been a focused theme in data mining research for over the past 15 years. Since the pioneering work of Agrawal, abundant literature has been dedicated to this research. In this article, we provide a brief overview of the current status of frequent pattern mining and discuss a few promising research directions.
---
paper_title: Data Mining Concepts and Techniques

paper_content:

Understand the need for analyses of large, complex, information-rich data sets. Identify the goals and primary tasks of the data-mining process. Describe the roots of data-mining technology. Recognize the iterative character of a data-mining process and specify its basic steps. Explain the influence of data quality on a data-mining process. Establish the relation between data warehousing and data mining. Data mining is an iterative process within which progress is defined by discovery, through either automatic or manual methods. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an "interesting" outcome. Data mining is the search for new, valuable, and nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers. In practice, the two primary goals of data mining tend to be prediction and description. Prediction involves using some variables or fields in the data set to predict unknown or future values of other variables of interest. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans. Therefore, it is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining, which produces new, nontrivial information based on the available data set.
---
paper_title: Multi Relational Mining in Network Intrusion Detection

paper_content:

The network intrusion detection (NIDS) is faced with the question to detect many kinds of intrusion. In order to detect the complex attack, network intrusion detection system need to analysis massive data captured form different network safety equipments. So a new multi relational mining algorithm MRA2 is proposed. MRA2 depend on the association rules mining technology and the probability function dependency method which is proposed through extending the theory of function dependency. MRA2 is able to synthesize the various datalog resources to detect intrusion effectively and reappear to the complex network attack scenario.
---
paper_title: A Brief Review on Frequent Pattern Mining

paper_content:

frequent pattern mining plays an essential role in association rule mining, which has been a focused theme in data mining research for over the past 15 years. Since the pioneering work of Agrawal, abundant literature has been dedicated to this research. In this article, we provide a brief overview of the current status of frequent pattern mining and discuss a few promising research directions.
---
paper_title: A novel associative classification algorithm: A combination of LAC and CMAR with new measure of weighted effect of each rule group

paper_content:

In recent, Association Classification not only has widely adopted but also has performed well in data mining. The literatures have been argued that the small disjunction and using multiple class-association rules have significant effect on classification accuracy. This paper is based on CMAR (Classification based on Multiple Class-Association Rules) and Adriano Veloso proposed Lazy Associative Classifier algorithm for Small Disjunction mining. In addition, we collocate with a new weight calculation method in our algorithm to solve weight bias problem of CMAR. This paper uses UCI 26 data set for experiment on our proposed algorithm. The finally results convincingly demonstrated that our proposed algorithm is high accuracy.
---
paper_title: Multi Relational Mining in Network Intrusion Detection

paper_content:

The network intrusion detection (NIDS) is faced with the question to detect many kinds of intrusion. In order to detect the complex attack, network intrusion detection system need to analysis massive data captured form different network safety equipments. So a new multi relational mining algorithm MRA2 is proposed. MRA2 depend on the association rules mining technology and the probability function dependency method which is proposed through extending the theory of function dependency. MRA2 is able to synthesize the various datalog resources to detect intrusion effectively and reappear to the complex network attack scenario.
---
paper_title: A High Performance Algorithm for Mining Frequent Patterns: LPS-Miner

paper_content:

In this paper, we present a novel frequent pattern mining algorithm, called LPS-Miner, which bases the pattern growth principle and uses two new data structures, LPS-FP-Tree (Light Partial-Support FP-Tree) and LPS-Forest (Light Partial-Support FP-Tree Forest) to present the database. LPS-FP-Tree is a variation of FP-Tree with lighter unidirectional nodes and the mining process depends on the partial-support of the patterns. LPS-Miner adopts partition and divide-and-conquer strategies in maximum, which decomposes the mining task into a set of smaller tasks. The light data structure and efficient memory management mechanism keep the memory usage stable and efficient. Other implementation-based optimizations, such as pruning and outputting-optimization, make the algorithm achieve high efficiency. We test our c++ implementation of this algorithm versus several other algorithms on four datasets. The experimental results show that our algorithm has better space and time efficiency.
---
paper_title: Constraint-Based Pattern Mining in Multi-relational Databases

paper_content:

We propose a new framework for constraint-based pattern mining in multi-relational databases. Distinguishing features of the framework are that (1) it allows finding patterns not only under anti-monotonic constraints, but also under monotonic constraints and closed ness constraints, among others, expressed over complex aggregates over multiple relations, (2) it builds on a declarative graphical representation of constraints that links closely to data models of multi-relational databases and constraint networks in constraint programming, (3) it maps multi-relational pattern mining tasks into constraint programs. Our framework builds on a unifying perspective of multi-relational pattern mining, relational database technology and constraint networks in constraint programming. We demonstrate our framework on IMDB and Finance multi-relational databases.
---
paper_title: Efficient Mining of Strong Negative Association Rules in Multi-Database

paper_content:

Strong Negative Association Rules can reveal irrelevances hidden between frequent itemsets. Existing research has made significant efforts in discovering both positive and negative association rules from single database. This paper presents an efficient method for mining strong negative association rules in multi-database. The method produces some strong negative relational patterns (a kind of infrequent itemsets) by pruning and scanning constructed Multi-Database Frequent Pattern tree, and extracts strong negative association rules according to the proposed correlation model. The experimental results show the effectiveness and efficiency of the proposed algorithm.
---

Title: A Review: Data Mining over Multi-Relations

Section 1: INTRODUCTION
Description 1: Discuss the challenges and importance of data mining in multi-relational databases, highlighting the issues with join operations and traditional association rule mining.

Section 2: DATA MINING
Description 2: Explain the concept of data mining, its applications, and the various techniques including multi-relational data mining. Address the steps involved in the data mining process and how it applies to various fields.

Section 3: MULTI RELATION CLASSIFICATION
Description 3: Detail the classification technique in data mining and its relevance in multi-relational contexts. Describe associative classification and the role of algorithms like CBA in building classifiers.

Section 4: FREQUENT PATTERN
Description 4: Outline the significance of frequent-pattern mining in data mining tasks, comparing algorithms like FP-Growth and Apriori. Discuss the efficiency and application areas of frequent-pattern mining.

Section 5: LITERATURE REVIEW
Description 5: Summarize the existing research and methodologies in multi-relation classification and frequent pattern mining. Highlight relevant studies and the contributions of different algorithms and techniques in this field.

Section 6: CONCLUSION
Description 6: Conclude the paper by summarizing the key points discussed and propose future directions to address current limitations, such as false negative rates and time complexity.

Section 7: ACKNOWLEDGEMENT
Description 7: Acknowledge the contributions and support from individuals and institutions that facilitated the research and writing of the paper.