Mining frequent itemsets with category-based constraints

Abstract

The discovery of frequent itemsets is a fundamental task of association rule mining. The challenge is the computational complexity of the itemset search space. One of the solutions for this is to use constraints to focus on some specific itemsets. In this paper, we propose a specific type of constraints called category-based as well as the associated algorithm for constrained rule mining based on Apriori. The Category-based Apriori algorithm reduces the computational complexity of the mining process by bypassing most of the subsets of the final itemsets. An experiment has been conducted to show the efficiency of the proposed technique. © Springer-Verlag Berlin Heidelberg 2003.