Visualization to Assist the Generation and Exploration of Association Rules

Visualization to Assist the Generation and Exploration of Association Rules

Claudio Haruo Yamamoto (Universidade de São Paulo, Brazil), Maria Cristina Ferreira de Oliveira (Universidade de São Paulo, Brazil) and Solange Oliveira Rezende (Universidade de São Paulo, Brazil)
DOI: 10.4018/978-1-60566-404-0.ch012
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Abstract

Miners face many challenges when dealing with association rule mining tasks, such as defining proper parameters for the algorithm, handling sets of rules so large that exploration becomes difficult and uncomfortable, and understanding complex rules containing many items. In order to tackle these problems, many researchers have been investigating visual representations and information visualization techniques to assist association rule mining. In this chapter, an overview is presented of the many approaches found in literature. First, the authors introduce a classification of the different approaches that rely on visual representations, based on the role played by the visualization technique in the exploration of rule sets. Current approaches typically focus on model viewing, that is visualizing rule content, namely antecedent and consequent in a rule, and/or different interest measure values associated to it. Nonetheless, other approaches do not restrict themselves to aiding exploration of the final rule set, but propose representations to assist miners along the rule extraction process. One such approach is a methodology the authors have been developing that supports visually assisted selective generation of association rules based on identifying clusters of similar itemsets. They introduce this methodology and a quantitative evaluation of it. Then, they present a case study in which it was employed to extract rules from a real and complex dataset. Finally, they identify some trends and issues for further developments in this area.
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Introduction

Huge volumes of data are now available, but we still face many difficulties in handling all such data to obtain actionable knowledge (Fayyad et al., 1996). Data visualization currently plays an important role in Knowledge Discovery processes, as it helps miners to create and validate hypotheses about the data and also to track and understand the behavior of mining algorithms (Oliveira & Levkowitz, 2003). Interactive visualization allows users to gain insight more easily by taking advantage of their vision system while performing complex investigation tasks. Combining the power of visual data exploration with analytical data mining, known as visual data mining (VDM), is now a trend.

Researchers (Ankerst, 2000, Oliveira & Levkowitz, 2003) identified three approaches for VDM: exploratory data visualization prior to mining, visualization of data mining models, and visualization of intermediate results or representations, during mining. The first approach concerns the use of visualization techniques during the data preprocessing stages of the knowledge discovery process. Visualization of data mining results focuses on visually representing the models extracted with data mining algorithms, to enhance comprehension. Finally, the third approach seeks to insert the miner into the knowledge discovery loop, not only to view intermediate patterns, but also to drive the process of exploring the solution space, for example, by providing feedback based on user previous knowledge about the domain or about the process itself.

Extracting association rules from a transaction database is a data mining task (association rule mining task) defined by Agrawal et al. (1993), in which the goal is to identify rules of the format A→B satisfying minimum support and minimum confidence values. A and B denote one or multiple items occurring in the transactions. The rule extraction problem is split into two main stages: (1) generating frequent itemsets; (2) extracting association rules from the frequent itemsets obtained. Several efficient algorithms have been designed for the first task, while the second one is actually trivial. A major problem, however, is that the process typically generates too many rules for analysis. Moreover, issues such as setting input parameters properly, understanding rules and identifying the interesting ones are also difficult. Visual representations have been proposed to tackle some of these problems. At first, visualization was employed mainly to assist miners in exploring the extracted rule set, based on visual representations of the rule space. There are also recent examples of employing visual representations to aid miners along the execution of the mining algorithm, e.g., to show intermediate results and to allow user feedback during the process.

This chapter is organized as follows. In the “Systematic survey” section we present a survey of approaches that employ visual representations in association rule mining. The survey is divided into two parts. In the first one, “Visualization of results of association rule mining”, we discuss contributions that employ information visualization techniques at the end of the mining process to visually represent its final results. In the second part, “Visualization during association rule mining”, we discuss contributions that employ visualization during the discovery process. In the third section, “An itemset-driven cluster-oriented approach”, we introduce a rule extraction methodology which aims at inserting the miner into the knowledge discovery loop. It adopts an interactive rule extraction algorithm coupled with a projection-based graph visualization of frequent itemsets, an itemset-based rule extraction approach and a visually-assisted pairwise comparison of rules for exploration of results. We also introduce the I2E System, that adopts the proposed methodology to enable an exploratory approach towards rule extraction, allowing miners to explore the rule space of a given transaction dataset. Furthermore, we present a quantitative evaluation of the methodology and a case study in which it is applied to a real and challenging dataset. Given the scenario, we then point out some challenges and trends for further developments in this area. Finally, we present some conclusions about this work.

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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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About the Contributors