An Improved Frequent Pattern-growth Approach to Discover Rare Association Rules

In this paper we have proposed an improved approach to extract rare association rules. The association rules which involve rare items are called rare association rules. Mining rare association rules is difficult with single minimum support (minsup) based approaches like Apriori and FP-growth as they suffer from “rare item problem” dilemma. At high minsup, frequent patterns involving rare items will be missed and at low minsup, the number of frequent patterns explodes. To address “rare item problem”, efforts have been made in the literature by extending the “multiple minimum support” framework to both Apriori and FP-growth approaches. The approaches proposed by extending “multiple minimum support” framework to Apriori require multiple scans on the dataset and generate huge number of candidate patterns. The approach proposed by extending the “multiple minimum support” framework to FP-growth is relatively efficient than Apriori based approaches, but suffers from performance problems. In this paper, we have proposed an improved multiple minimum support based FP-growth approach by exploiting the notions such as “least minimum support” and “infrequent leaf node pruning”. Experimental results on both synthetic and real world datasets show that the proposed approach improves the performance over existing approaches.