Towards Efficient Mining of Periodic-Frequent Patterns in Transactional Databases

Abstract

Periodic-Frequent patterns are an important class of regularities that exist in a transactional database. A pattern is periodic-frequent if it satisfies both minimum support (minsup) and maximum periodicity (maxprd) constraints. Minsup constraint controls the minimum number of transactions that a pattern must cover in a database. Maxprd constraint controls the maximum duration between the two transactions below which a pattern should reoccur in a database. In the literature an approach has been proposed to extract periodic-frequent patterns using single minsup and single maxprd constraints. However, real-world databases are mostly non-uniform in nature containing both frequent and relatively infrequent (or rarely) occurring items. Researchers are making efforts to propose improved approaches for extracting frequent patterns that contain rare items as they contain useful knowledge. For mining periodic patterns that contain frequent and rare items we have to specify low minsup and high maxprd. It is difficult to mine periodic-frequent patterns because the low minsup and high maxprd can cause combinatorial explosion. In this paper we propose an improved approach which facilitates the user to specify different minsup and maxprd values for each pattern depending upon the items within it. Also, we present an efficient pattern growth approach and a methodology to dynamically specify maxprd for each pattern. Experimental results show that the proposed approach is efficient.KeywordsData miningfrequent patternrare periodic-frequent patternmultiple constraints