Locating motifs in time-series data

Authors:
Zheng Liu;Jeffrey Xu Yu;Xuemin Lin;Hongjun Lu;Wei Wang
Affiliations:
The University of News South Wales, Sydney, Australia;The Chinese University of Hong Kong, Hong Kong, China;The University of News South Wales, Sydney, Australia;The Hong Kong University of Science and Technology, Hong Kong, China;The University of News South Wales, Sydney, Australia
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

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Abstract

Finding motifs in time-series is proposed to make clustering of time-series subsequences meaningful, because most existing algorithms of clustering time-series subsequences are reported meaningless in recent studies. The existing motif finding algorithms emphasize the efficiency at the expense of quality, in terms of the number of time-series subsequences in a motif and the total number of motifs found. In this paper, we formalize the problem as a continuous top-k motif balls problem in an m-dimensional space, and propose heuristic approaches that can significantly improve the quality of motifs with reasonable overhead, as shown in our experimental studies.