Efficiently mining Maximal Frequent Sets in dense databases for discovering association rules

Article type: Research Article

Authors: Srikumar, Krishnamoorthy^{; *} | Bhasker, Bharat

Affiliations: Indian Institute of Management, Prabandh Nagar, Lucknow, 226 013, India. Tel.: +91 0522 2734101 to +91 0522 2734123; Fax: +91 0522 2734025; E-mail: srikumar@iiml.ac.in, bhasker@iiml.ac.in

Correspondence: [*] Corresponding author

Abstract: We present, MaxDomino, an algorithm for mining Maximal Frequent Sets (MFS) for discovering association rules in dense databases. The algorithm uses novel concepts of dominancy factor and collapsibility of transaction for efficiently mining MFS. Unlike traditional bottom up approach with look-aheads, MaxDomino employs a top down strategy with selective bottom-up search for mining MFS. Using a set of benchmark dense datasets–created by University of California, Irvine–we demonstrate that MaxDomino outperforms GenMax–that performs better compared to other known algorithms–at higher support levels. Our algorithm is especially efficient for dense databases.

Keywords: association rules, maximal frequent sets, dominancy factor

DOI: 10.3233/IDA-2004-8205

Journal: Intelligent Data Analysis, vol. 8, no. 2, pp. 171-182, 2004