An Itemset-Driven Cluster-Oriented Approach to Extract Compact and Meaningful Sets of Association Rules

Abstract

Extracting association rules from large datasets typically results in a huge amount of rules. An approach to tackle this problem is to filter the resulting rule set, which reduces the rules, at the cost of also eliminating potentially interesting ones. In exploring a new dataset in search of relevant associations, it may be more useful for miners to have an overview of the space of rules obtainable from the dataset, rather than getting an arbitrary set satisfying high values for given interest measures. We describe a rule extraction approach that favors rule diversity, allowing miners to gain an overview of the rule space while reducing semantic redundancy within the rule set. This approach adopts an itemset-driven rule generation coupled with a cluster-based filtering process. The set of rules so obtained provides a starting point for a user-driven exploration of it.