Hierarchical Clustering for Thematic Browsing and Summarization of Large Sets of Association Rules

In this paper we propose a method for grouping and summarizing large sets of association rules according to the items contained in each rule. We use hierarchical clustering to partition the initial rule set into thematically coherent subsets. This enables the summarization of the rule set by adequately choosing a representative rule for each subset, and helps in the interactive exploration of the rule model by the user. We define the requirements of our approach, and formally show the adequacy of the chosen approach to our aims. Rule clusters can also be used to infer novel interest measures for the rules. Such measures are based on the lexicon of the rules and are complementary to measures based on statistical properties, such as confidence, lift and conviction. We show examples of the application of the proposed techniques.