Meta-Knowledge Based Approach for an Interactive Visualization of Large Amounts of Association Rules

Meta-Knowledge Based Approach for an Interactive Visualization of Large Amounts of Association Rules

Sadok Ben Yahia (Faculty of Sciences of Tunis, Tunisia), Olivier Couturier (Centre de Recherche en Informatique de Lens (CRIL), France), Tarek Hamrouni (Faculty of Sciences of Tunis, Tunisia) and Engelbert Mephu Nguifo (Centre de Recherche en Informatique de Lens (CRIL), France)
DOI: 10.4018/978-1-60566-404-0.ch011
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Providing efficient and easy-to-use graphical tools to users is a promising challenge of data mining, especially in the case of association rules. These tools must be able to generate explicit knowledge and, then, to present it in an elegant way. Visualization techniques have shown to be an efficient solution to achieve such a goal. Even though considered as a key step in the mining process, the visualization step of association rules received much less attention than that paid to the extraction step. Nevertheless, some graphical tools have been developed to extract and visualize association rules. In those tools, various approaches are proposed to filter the huge number of association rules before the visualization step. However both data mining steps (association rule extraction and visualization) are treated separately in a one way process. Recently different approaches have been proposed that use meta-knowledge to guide the user during the mining process. Standing at the crossroads of Data Mining and Human-Computer Interaction, those approaches present an integrated framework covering both steps of the data mining process. This chapter describes and discusses such approaches. Two approaches are described in details: the first one builds a roadmap of compact representation of association rules from which the user can explore generic bases of association rules and derive, if desired, redundant ones without information loss. The second approach clusters the set of association rules or its generic bases, and uses a fisheye view technique to help the user during the mining of association rules. Generic bases with their links or the associated clusters constitute the meta-knowledge used to guide the interactive and cooperative visualization of association rules.
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Data mining techniques have been proposed and studied to help users better understand and scrutinize huge amounts of collected and stored data. In this respect, extracting association rules has grasped the interest of the data mining community. Thus, the last decade has been marked by a determined algorithmic effort to reduce the computation time of the interesting itemset extraction step. The obtained success is primarily due to an important programming effort combined with strategies for compacting data structures in main memory. However, it seems obvious that this frenzied activity loses sight of the essential objective of this step, i.e., extracting a reliable knowledge, of exploitable size for users. Indeed, the unmanageably large association rule sets compounded with their low precision often make the perusal of knowledge ineffective, their exploitation time-consuming and frustrating for users. Moreover, unfortunately, this teenaged field seems to provide results in the opposite direction with the evolving “knowledge management” topic.

The commonly generated thousands and even millions of high-confidence rules – among which many are redundant (Bastide et al., 2000; Ben Yahia et al., 2006; Stumme et al., 2001; Zaki, 2004) – encouraged the development of more acute techniques to limit the number of reported rules, starting by basic pruning techniques based on thresholds for both the frequency of the represented pattern and the strength of the dependency between premise and conclusion. Moreover, this pruning can be based on patterns defined by the user (user-defined templates), on Boolean operators (Meo et al., 1996; Ng et al., 1998; Ohsaki et al., 2004; Srikant et al., 1997). The number of rules can be reduced through pruning based on additional information such as a taxonomy on items (Han, & Fu, 1995) or on a metric of specific interest (Brin et al., 1997) (e.g., Pearson’s correlation or χ2-test). More advanced techniques that produce only lossless information limited number of the entire set of rules, called generic bases (Bastide et al., 2000). The generation of such generic bases heavily draws on a battery of results provided by formal concept analysis (FCA) (Ganter & Wille, 1999). This association rule reduction can be seen as a “sine qua non” condition to avoid that the visualization step comes up short in dealing with large amounts of rules. In fact, the most used kind of visualization categories in data mining is the use of visualization techniques to present the information caught out from the mining process. Graphical visualization tools became more appealing when handling large data sets with complex relationships, since information presented in the form of images is more direct and easily understood by humans (Buono & Costabile, 2004; Buono et al., 2001). Visualization tools allow users to work in an interactive environment with ease in understanding rules.

Consequently, data mining techniques gain in presenting discovered knowledge in an interactive, graphical form that often fosters new insights. Thus, the user is encouraged to form and validate new hypotheses to the end of better problem solving and gaining deeper domain knowledge (Bustos et al., 2003). Visual data analysis is a way to achieve such goal, especially when it is tightly coupled with the management of meta-knowledge used to handle the vast amounts of extracted knowledge.

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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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