Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms
Rage Uday Kiran, P. Krishna Reddy · 2011
Frequent patterns are an important class of regularities that exist in a transaction database. Certain frequent patterns with low minimum support (minsup) value can provide useful information in many real-world applications. However, extraction of these frequent patterns with single minsupbased frequent pattern mining algorithms such as Apriori and FP-growth leads to “rare item problem. ” That is, at high minsup value, the frequent patterns with low minsup are missed, and at low minsup value, the number of frequent patterns explodes. In the literature,“multiple minsups framework” was proposed to discover frequent patterns. Furthermore, frequent pattern mining techniques such as Multiple Support Apriori and Conditional Frequent Pattern-growth (CFP-growth) algorithms have been proposed. As the frequent patterns mined with this framework do not satisfy downward closure property, the algorithms follow different types of pruning techniques to reduce the search space. In this paper, we propose an efficient CFP-growth algorithm by proposing new pruning techniques. Experimental results show that the proposed pruning techniques are effective.