Mining Dense Periodic Patterns in Time Series Databases

Mining Dense Periodic Patterns in Time Series Databases

Wynne Hsu (National University of Singapore, Singapore), Mong Li Lee (National University of Singapore, Singapore) and Junmei Wang (National University of Singapore, Singapore)
Copyright: © 2008 |Pages: 19
DOI: 10.4018/978-1-59904-387-6.ch003
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Abstract

In this chapter, we describe a new periodicity detection algorithm to efficiently discover short period patterns that may exist in only a limited range of the time series. We refer to these patterns as the dense periodic patterns, where the periodicity is focused on part of the time series. We present a dense periodic pattern mining algorithm called DPMiner to find dense periodic patterns, and design a pruning strategy to limit the search space to the feasible periods. Experimental results on both real-life and synthetic datasets indicate that DPMiner is both scalable and efficient.

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