An Efficient Approach to Mine Periodic-Frequent Patterns in Transactional Databases

Abstract

Recently, temporal occurrences of the frequent patterns in a transactional database has been exploited as an interestingness criterion to discover a class of user-interest-based frequent patterns, called periodic-frequent patterns. Informally, a frequent pattern is said to be periodic-frequent if it occurs at regular intervals specified by the user throughout the database. The basic model of periodic-frequent patterns is based on the notion of “single constraints.” The use of this model to mine periodic-frequent patterns containing both frequent and rare items leads to a dilemma called the “rare item problem.” To confront the problem, an alternative model based on the notion of “multiple constraints” has been proposed in the literature. The periodic-frequent patterns discovered with this model do not satisfy downward closure property. As a result, it is computationally expensive to mine periodic-frequent patterns with the model. Furthermore, it has been observed that this model still generates some uninteresting patterns as periodic-frequent patterns. With this motivation, we propose an efficient model based on the notion of “multiple constraints.” The periodic-frequent patterns discovered with this model satisfy downward closure property. Hence, periodic-frequent patterns can be efficiently discovered. A pattern-growth algorithm has also been discussed for the proposed model. Experimental results show that the proposed model is effective.