Mining Frequent Itemsets with Category-Based Constraints

Do, T. D., Hui, S. C. and Fong, A. (2003) Mining Frequent Itemsets with Category-Based Constraints. In: 6th International Conference on Discovery Science, Sapporo, Japan, 17-19 Oct 2003, pp. 76-86. ISBN 9783540396444 (doi: 10.1007/978-3-540-39644-4_8)

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.

• Item Type: Conference Proceedings
• Additional Information: Lecture notes in computer science: 2843
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Fong, Dr Alvis Cheuk Min
• Authors: Do, T. D., Hui, S. C., and Fong, A.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 0302-9743
• ISBN: 9783540396444