Description: Periodic pattern mining is the problem that regards tempo-
ral regularity. There are many emerging applications in periodic pattern
mining, including web usage recommendation, weather prediction, com-
puter networks and biological data. In this paper, we propose a Pro-
gressive Timelist-Based Veri cation (PTV) method to the mining of pe-
riodic patterns from a sequence of event sets. The parameter min rep,
is employed to specify the minimum number of repetitions required for
a valid segment of non-disrupted pattern occurrences. We also describe
a partitioning approach to handle extra large/long data sequence. The
experiments demonstrate good performance and scalability with large
frequent patterns.
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