Abstract

Mining Frequent Patterns in transaction database TD has been studied extensively in data mining research. However, most of the existing frequent pattern mining algorithm does not consider the time stamps associated with the transactions. Temporal periodicity of pattern appearance can be regarded as an important criterion for measuring the interestingness of frequent patterns in several applications. In this paper, we extend the existing frequent pattern mining framework to take into account the time stamp as periodicity i.e., the time stamp from the month January to June is as First Period  and from July to December as Second Period , and discover frequent patterns for each period. An efficient tree based data structure called periodic-frequent pattern tree that captures the database TD in a highly compact manner and enables a pattern growth mining techniques to generate the complete set of Periodic-Frequent patterns. Example illustrating the proposed approach is given. The characteristics of the algorithm are discussed.

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