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

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.