SETM*-Lmax: An EÆcient Set-Based Approach to Find Maximal Large Itemsets

Abstract

Discovery of association rules is an important problem in the area of data mining. An association rule means that the presence of some items in a transaction will imply the presence of other items in the same transaction. For this problem, how to eÆciently count large itemsets is the major work, where a large itemset is a set of items appearing in a suÆcient number of transactions. In this paper, we propose an eÆcient SETM*-Lmax algorithm to nd maximal large itemsets, based on a high-level set-based approach. The advantage of the set-based approach, like the SETM algorithm, is simple and stable over the range of parameter values. In the SETM*-Lmax algorithm, we use a forward approach to nd all maximal large itemsets from Lk, and the w-itemset is not included in the w- subsets of the j-itemset, where 1 k MaxK, 1 w < j MaxK, LMaxK 6= ; and LMaxK+1 = ;. We conduct several experiments using dierent synthetic relational databases. The simulation results show that the proposed forward approach (SETM*- Lmax) to nd all maximal large itemsets requires shorter time than the backward approach proposed by Agrawal.