Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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
Towards efficient mining of periodic-frequent patterns in transactional databases
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Fast tree-based mining of frequent itemsets from uncertain data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
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Periodic-frequent patterns are a class of user-interest-based frequent patterns that exist in a transactional database. A frequent pattern can be said periodic-frequent if it appears at a regular user-specified interval in a database. In the literature, an approach has been proposed to extract periodic-frequent patterns that occur periodically throughout the database. However, it is generally difficult for a frequent pattern to appear periodically throughout the database without any interruption in many real-world applications. In this paper, we propose an improved approach by introducing a new interestingness measure to discover periodic-frequent patterns that occur almost periodically in the database. A pattern-growth algorithm has been proposed to discover the complete set of periodic-frequent patterns. Experimental results show that the proposed model is effective.