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.