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
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Discovering Periodic-Frequent Patterns in Transactional Databases
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining rare association rules in the datasets with widely varying items' frequencies
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Proceedings of the 14th International Conference on Extending Database Technology
An alternative interestingness measure for mining periodic-frequent patterns
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Efficient mining top-k regular-frequent itemset using compressed tidsets
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
An efficient approach to mine periodic-frequent patterns in transactional databases
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
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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. M axprd 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.