PFPM: Discovering Periodic Frequent Patterns with Novel Periodicity Measures

Periodic pattern mining is the task of discovering patterns that periodically appear in transactions. Typically, periodic pattern mining algorithms will discard a pattern as being nonperiodic if it has a single period greater than a maximal periodicity threshold, defined by the user. A major drawback of this approach is that it is not flexible, as a pattern can be discarded based on only one of its periods. In this chapter, we present a solution to this issue by proposing to discover periodic patterns using three measures: the minimum periodicity, the maximum periodicity, and the average periodicity. The combination of these measures has the advantage of being more flexible. Properties of these measures are studied. Moreover, an efficient algorithm named PFPM (Periodic Frequent Pattern Miner) is proposed to discover all frequent periodic patterns using these measures. An experimental evaluation on real data sets shows that the proposed PFPM algorithm is efficient and can filter a huge number of nonperiodic patterns to reveal only the desired periodic patterns.