Title: A GA-based approach for finding appropriate granularity levels of patterns from time series

Authors: Chun-Hao Chen; Vincent S. Tseng; Hsieh-Hui Yu; Tzung-Pei Hong; Neil Y. Yen

Addresses: Department of Computer Science and Information Engineering, Tamkang University, Taipei 251, Taiwan ' Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan ' Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan ' Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan; Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan ' School of Computer Science and Engineering, University of Aizu, Fukushima, Japan

Abstract: In our previous approach, we proposed an algorithm for finding segments and patterns simultaneously from a given time series. In that approach, because patterns were derived through clustering techniques, the number of clusters was hard to be setting. In other words, the granularity of derived patterns was not taken into consideration. Hence, an approach for deriving appropriate granularity levels of patterns is proposed in this paper. The cut points of a time series are first encoded into a chromosome. Each two adjacent cut points represents a segment. The segments in a chromosome are then divided into groups using the cluster affinity search technique with a similarity matrix and an affinity threshold. With the affinity threshold, patterns with the desired granularity level can be derived. Experiments on a real dataset are also conducted to demonstrate the effectiveness of the proposed approach.

Keywords: genetic algorithms; time series segmentation; clustering; PIPs; perceptually important points; granularity levels; patterns; cut points; cluster affinity search; affinity threshold.

DOI: 10.1504/IJWGS.2016.079159

International Journal of Web and Grid Services, 2016 Vol.12 No.3, pp.217 - 239

Received: 13 Nov 2015
Accepted: 02 Jun 2016

Published online: 14 Sep 2016 *

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