Frequent Closed Itemsets Based Condensed Representations for Association Rules

Abstract

After more than one decade of researches on association rule mining, efficient and scalable techniques for the discovery of relevant association rules from large high-dimensional datasets are now available. Most initial studies have focused on the development of theoretical frameworks and efficient algorithms and data structures for association rule mining. However, many applications of association rules to data from different domains have shown that techniques for filtering irrelevant and useless association rules are required to simplify their interpretation by the end-user. Solutions proposed to address this problem can be classified in four main trends: constraint-based mining, interestingness measures, association rule structure analysis, and condensed representations. This chapter focuses on condensed representations that are characterized in the frequent closed itemset framework to expose their advantages and drawbacks.