Frequent Closed Itemsets Based Condensed Representations for Association Rules

Frequent Closed Itemsets Based Condensed Representations for Association Rules

Nicolas Pasquier (University of Nice Sophia-Antipolis, France)
DOI: 10.4018/978-1-60566-404-0.ch013
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Abstract

After more than one decade of researches on association rule mining, efficient and scalable techniques for the discovery of relevant association rules from large high-dimensional datasets are now available. Most initial studies have focused on the development of theoretical frameworks and efficient algorithms and data structures for association rule mining. However, many applications of association rules to data from different domains have shown that techniques for filtering irrelevant and useless association rules are required to simplify their interpretation by the end-user. Solutions proposed to address this problem can be classified in four main trends: constraint-based mining, interestingness measures, association rule structure analysis, and condensed representations. This chapter focuses on condensed representations that are characterized in the frequent closed itemset framework to expose their advantages and drawbacks.
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Association Rule Mining

In order to improve the extraction efficiency, most algorithms for mining association rules operate on binary data represented in a transactional or binary format. This also enables the treatment of mixed data types, resulting from the integration of multiple data sources for example, with the same algorithm. The transactional and binary representations of the example dataset D, used as a support in the rest of the chapter, are shown in Table 1. In the transactional or enumeration format represented in Table 1(a) each object, called transaction or data line, contains a list of items. In the binary format represented in Table 1(b) each object2 is a bit vector and each bit indicates if the object contains the corresponding item or not.

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Table of Contents
Foreword
David Bell
Acknowledgment
Yanchang Zhao, Chengqi Zhang, Longbing Cao
Chapter 1
Paul D. McNicholas, Yanchang Zhao
Association rules present one of the most versatile techniques for the analysis of binary data, with applications in areas as diverse as retail... Sample PDF
Association Rules: An Overview
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Chapter 2
Mirko Boettcher, Georg Ruß, Detlef Nauck, Rudolf Kruse
Association rule mining typically produces large numbers of rules, thereby creating a second-order data mining problem: which of the generated rules... Sample PDF
From Change Mining to Relevance Feedback: A Unified View on Assessing Rule Interestingness
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Chapter 3
Solange Oliveira Rezende, Edson Augusto Melanda, Magaly Lika Fujimoto, Roberta Akemi Sinoara, Veronica Oliveira de Carvalho
Association rule mining is a data mining task that is applied in several real problems. However, due to the huge number of association rules that... Sample PDF
Combining Data-Driven and User-Driven Evaluation Measures to Identify Interesting Rules
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Chapter 4
Julien Blanchard, Fabrice Guillet, Pascale Kuntz
Assessing rules with interestingness measures is the cornerstone of successful applications of association rule discovery. However, as numerous... Sample PDF
Semantics-Based Classification of Rule Interestingness Measures
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Chapter 5
Huawen Liu, Jigui Sun, Huijie Zhang
In data mining, rule management is getting more and more important. Usually, a large number of rules will be induced from large databases in many... Sample PDF
Post-Processing for Rule Reduction Using Closed Set
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Chapter 6
Hacène Cherfi, Amedeo Napoli, Yannick Toussaint
A text mining process using association rules generates a very large number of rules. According to experts of the domain, most of these rules... Sample PDF
A Conformity Measure Using Background Knowledge for Association Rules: Application to Text Mining
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Chapter 7
Hetal Thakkar, Barzan Mozafari, Carlo Zaniolo
The real-time (or just-on-time) requirement associated with online association rule mining implies the need to expedite the analysis and validation... Sample PDF
Continuous Post-Mining of Association Rules in a Data Stream Management System
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Chapter 8
Ronaldo Cristiano Prati
Receiver Operating Characteristics (ROC) graph is a popular way of assessing the performance of classification rules. However, as such graphs are... Sample PDF
QROC: A Variation of ROC Space to Analyze Item Set Costs/Benefits in Association Rules
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Chapter 9
Maria-Luiza Antonie, David Chodos, Osmar Zaïane
The chapter introduces the associative classifier, a classification model based on association rules, and describes the three phases of the model... Sample PDF
Variations on Associative Classifiers and Classification Results Analyses
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Chapter 10
Silvia Chiusano, Paolo Garza
In this chapter the authors make a comparative study of five well-known classification rule pruning methods with the aim of understanding their... Sample PDF
Selection of High Quality Rules in Associative Classification
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Chapter 11
Sadok Ben Yahia, Olivier Couturier, Tarek Hamrouni, Engelbert Mephu Nguifo
Providing efficient and easy-to-use graphical tools to users is a promising challenge of data mining, especially in the case of association rules.... Sample PDF
Meta-Knowledge Based Approach for an Interactive Visualization of Large Amounts of Association Rules
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Chapter 12
Claudio Haruo Yamamoto, Maria Cristina Ferreira de Oliveira, Solange Oliveira Rezende
Miners face many challenges when dealing with association rule mining tasks, such as defining proper parameters for the algorithm, handling sets of... Sample PDF
Visualization to Assist the Generation and Exploration of Association Rules
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Chapter 13
Nicolas Pasquier
After more than one decade of researches on association rule mining, efficient and scalable techniques for the discovery of relevant association... Sample PDF
Frequent Closed Itemsets Based Condensed Representations for Association Rules
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Chapter 14
Mengling Feng, Jinyan Li, Guozhu Dong, Limsoon Wong
This chapter surveys the maintenance of frequent patterns in transaction datasets. It is written to be accessible to researchers familiar with the... Sample PDF
Maintenance of Frequent Patterns: A Survey
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Chapter 15
Guozhu Dong, Jinyan Li, Guimei Liu, Limsoon Wong
This chapter considers the problem of “conditional contrast pattern mining.” It is related to contrast mining, where one considers the mining of... Sample PDF
Mining Conditional Contrast Patterns
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Chapter 16
Qinrong Feng, Duoqian Miao, Ruizhi Wang
Decision rules mining is an important technique in machine learning and data mining, it has been studied intensively during the past few years.... Sample PDF
Multidimensional Model-Based Decision Rules Mining
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