CloseMiner: Discovering Frequent Closed Itemsets using Frequent Closed Tidsets Gourakishwar Ningthoujam, Ranbir Sanasam, and Anjana Kakoti   View Paper (PDF)   Return to Session 13: Learning Techniques II Abstract Complete set of itemsets can be grouped into non-overlapping clusters identified by closed tidsets. Each cluster has only one closed itemset and is the superset of all itemsets with the same support. Number of closed itemsets is identical to the number of clusters. Therefore, the problem of discovering closed itemsets can be considered as the problem of clustering the complete set of itemsets by closed tidsets. In this paper, we present CloseMiner, a new algorithm for discovering all frequent closed itemsets by grouping the complete set of itemsets into non-overlapping clusters identified by closed tidsets. An extensive experimental evaluation on a number of real and synthetic databases shows that CloseMiner outperforms Apriori and CHARM. ©2006 Association for Computing Machinery