Post-processing Operators for Browsing Large Sets of Association Rules

Abstract

Association rule engines typically output a very large set of rules. Despite the fact that association rules are regarded as highly comprehensible and useful for data mining and decision support in fields such as marketing, retail, demographics, among others, lengthy outputs may discourage users from using the technique. In this paper we propose a post-processing methodology and tool for browsing/visualizing large sets of association rules. The method is based on a set of operators that transform sets of rules into sets of rules, allowing focusing on interesting regions of the rule space. Each set of rules can be then seen with different graphical representations. The tool is web-based and uses SVG. Association rules are given in PMML