An Efficient Pruning and Filtering Strategy to Mine Partial Periodic Patterns from a Sequence of Event Sets

Kung-Jiuan Yang, Tzung-Pei Hong, Yuh-Min Chen and Guo-Cheng Lan
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Kung-Jiuan Yang: Department of Information Management, Fortune Institute of Technology, Daliao District, Kaohsiung, Taiwan
Tzung-Pei Hong: Department of Computer Science and Information Engineering, National University of Kaohsiung, Nan-Tzu District, Kaohsiung, Taiwan
Yuh-Min Chen: Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan City, Taiwan
Guo-Cheng Lan: Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu County, Taiwan

International Journal of Data Warehousing and Mining (IJDWM), 2014, vol. 10, issue 2, 18-38

Abstract: Partial periodic patterns are commonly seen in real-world applications. The major problem of mining partial periodic patterns is the efficiency problem due to a huge set of partial periodic candidates. Although some efficient algorithms have been developed to tackle the problem, the performance of the algorithms significantly drops when the mining parameters are set low. In the past, the authors have adopted the projection-based approach to discover the partial periodic patterns from single-event time series. In this paper, the authors extend it to mine partial periodic patterns from a sequence of event sets which multiple events concurrently occur at the same time stamp. Besides, an efficient pruning and filtering strategy is also proposed to speed up the mining process. Finally, the experimental results on a synthetic dataset and real oil price dataset show the good performance of the proposed approach.

Date: 2014
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