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Visualization to Assist the Generation and Exploration of Association Rules

Visualization to Assist the Generation and Exploration of Association Rules

Claudio Haruo Yamamoto, Maria Cristina Ferreira de Oliveira, Solange Oliveira Rezende
Copyright: © 2009 |Pages: 22
ISBN13: 9781605664040|ISBN10: 1605664049|EISBN13: 9781605664057
DOI: 10.4018/978-1-60566-404-0.ch012
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MLA

Yamamoto, Claudio Haruo, et al. "Visualization to Assist the Generation and Exploration of Association Rules." Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, edited by Yanchang Zhao, et al., IGI Global, 2009, pp. 224-245. https://doi.org/10.4018/978-1-60566-404-0.ch012

APA

Yamamoto, C. H., Ferreira de Oliveira, M. C., & Rezende, S. O. (2009). Visualization to Assist the Generation and Exploration of Association Rules. In Y. Zhao, C. Zhang, & L. Cao (Eds.), Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction (pp. 224-245). IGI Global. https://doi.org/10.4018/978-1-60566-404-0.ch012

Chicago

Yamamoto, Claudio Haruo, Maria Cristina Ferreira de Oliveira, and Solange Oliveira Rezende. "Visualization to Assist the Generation and Exploration of Association Rules." In Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, edited by Yanchang Zhao, Chengqi Zhang, and Longbing Cao, 224-245. Hershey, PA: IGI Global, 2009. https://doi.org/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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