An Alternative Interestingness Measure for Mining Periodic-Frequent Patterns

Abstract

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.