Association Rules: An Overview

Association Rules: An Overview

Paul D. McNicholas (University of Guelph, Ontario, Canada) and Yanchang Zhao (University of Technology, Sydney, Australia)
DOI: 10.4018/978-1-60566-404-0.ch001
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Association rules present one of the most versatile techniques for the analysis of binary data, with applications in areas as diverse as retail, bioinformatics, and sociology. In this chapter, the origin of association rules is discussed along with the functions by which association rules are traditionally characterised. Following the formal definition of an association rule, these functions – support, confidence and lift – are defined and various methods of rule generation are presented, spanning 15 years of development. There is some discussion about negations and negative association rules and an analogy between association rules and 2×2 tables is outlined. Pruning methods are discussed, followed by an overview of measures of interestingness. Finally, the post-mining stage of the association rule paradigm is put in the context of the preceding stages of the mining process.
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In general, association rules present an efficient method of analysing very large binary, or discretized, data sets. One common application is to discover relationships between binary variables in transaction databases, and this type of analysis is called a ‘market basket analysis’. While association rules have been used to analyse non-binary data, such analyses typically involve the data being coded as binary before proceeding. Association rules present one of the most versatile methods of analysing large binary datasets; recent applications have ranged from detection of bio-terrorist attacks (Fienberg and Shmeeli, 2005) and the analysis of gene expression data (Carmona-Saez et al., 2006), to the analysis of Irish third level education applications (McNicholas, 2007).

There are two or three steps involved in a typical association rule analysis: (Box 1)

Box 1.
Steps involved in a typical association rule analysis

Coding of data as binary (if data is not binary)

Rule generation


This book focuses on the third step, post-mining, and the purpose of this chapter is to set the scene for this focus. This chapter begins with a look back towards the foundations of thought on the association of attributes; the idea of an association rule is then introduced, followed by discussion about rule generation. Finally, there is a broad review of pruning and interestingness.



Although formally introduced towards the end of the twentieth century (Agrawal et al. 1993), many of the ideas behind association rules can be seen in the literature over a century earlier. Yule (1903) wrote about associations between attributes and, in doing so, he built upon the earlier writings of De Mogran (1847), Boole (1847, 1854) and Jevons (1890). Although the premise was the analysis of non-binary data that is converted into binary data, Yule (1903) raised many of the issues that are now central to association rule analysis. Furthermore, Yule (1903) built the idea of not possessing an attribute into his paradigm, from the outset, as an important concept. Yet, it was several years after the introduction of association rules before such issues were seriously considered and it is still the case that the absence of items from rules is often ignored in analyses. We will revisit this issue later in this chapter, when discussing negative association rules and negations.

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Editorial Advisory Board
Table of Contents
David Bell
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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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