Mining Rare Periodic-Frequent Patterns Using Multiple Minimum Supports.
Rage Uday Kiran, P. Krishna Reddy · Conference on Management of Data · 2009
Recently, an approach has been proposed in the literature to extract frequent patterns which occur periodically. In this paper, we have proposed an approach to extract rare periodic-frequent patterns. Normally, the single minsup based frequent pattern mining approaches like Apriori and FP-growth suffer from “rare item problem”. That is, at high minsup, frequent patterns consisting of rare items will be missed, and at low minsup, number of frequent patterns explode. In the literature, efforts have been made to extract rare frequent patterns under “multiple minimum support framework”. It was observed that the periodic-frequent pattern mining approach also suffers from the “rare item problem”. In this paper, we have extended “multiple minimum support framework” to extract rare periodic-frequent patterns and developed a new algorithm to extract rare periodic-frequent patterns. Experiment results show that the proposed approach is efficient.