Mining periodic-frequent patterns with maximum items' support constraints

R. Uday Kiran, P. Krishna Reddy
2010 Proceedings of the Third Annual ACM Bangalore Conference on - COMPUTE '10  
The single minimum support (minsup) based frequent pattern mining approaches like Apriori and FP-growth suffer from "rare item problem" while extracting frequent patterns. 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". In this framework, "rare frequent patterns" can be extracted by
more » ... ifying 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 patterns which occur periodically. The basic model of periodic-frequent patterns is based on single minsup constraint. It was observed that the periodic-frequent pattern mining approach also suffers from the "rare item problem". An effort has been made to extract rare periodic-frequent patterns using minimum constraint model. In this paper, we have proposed a pattern-growth approach to extract rare periodic-frequent patterns by specifying minsup under maximum constraint model. Experiment results show that the proposed approach is efficient.
doi:10.1145/1754288.1754289 dblp:conf/compute/KiranR10 fatcat:pjalwkhca5ewnprd6heqpukdkm