Mining periodic-frequent patterns with maximum items' support constraints
Rage Uday Kiran, P. Krishna Reddy · 2010
The single minimum support (minsup) based frequent pattern mining approaches like Apriori and FP-growth suffer from item problem while extracting frequent patterns. That is, at high minsup, frequent consisting of rare items will be missed, and at low minsup, number of frequent explode. In the literature, efforts have been made to extract rare frequent under multiple minimum support framework. In this framework, frequent patterns can be extracted by specifying minsup of the pattern using two models: minimum constraint model and maximum constraint model. In the literature, an approach has been proposed to extract only those frequent which occur periodically. The basic model of periodic-frequent is based on single minsup constraint. It was observed that the periodic-frequent pattern mining approach also suffers from the item problem. An effort has been made to extract rare periodic-frequent using minimum constraint model. In this paper, we have proposed a pattern-growth approach to extract rare periodic-frequent by specifying minsup under maximum constraint model. Experiment results show that the proposed approach is efficient.