A new data structure for asynchronous periodic pattern mining
Jieh-Shan Yeh, Szu-Chen Lin · 2009
The periodic pattern mining is to discover valid periodic patterns in a time-related dataset. Previous studies mostly concern the synchronous periodic patterns. There are many methods for mining periodic patterns proposed in literature. Nevertheless, asynchronous periodic pattern mining gradually receives more and more attention recently. In this paper, we propose an efficient linked structure and the OEOP algorithm to discover all kinds of valid segments in each single event sequence. Then, refer to the general model of asynchronous periodic pattern mining proposed by Huang and Chang, we combine these valid segments found by OEOP into 1-patterns with multiple events, multiple patterns with multiple events and asynchronous periodic patterns. Besides, we implement these algorithms on two real datasets. The experimental results show that these algorithms have the good performance and scalability.