Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Mining association rules with multiple minimum supports using maximum constraints
International Journal of Approximate Reasoning
Discovering diverse-frequent patterns in transactional databases
Proceedings of the 17th International Conference on Management of Data
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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 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 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.