Motif-based method for initialization the k-means clustering for time series data

Authors:
Le Phu;Duong Tuan Anh
Affiliations:
Faculty of Computer Science and Technology, Ho Chi Minh City University of Technology, Vietnam;Faculty of Computer Science and Technology, Ho Chi Minh City University of Technology, Vietnam
Venue:
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Year:
2011

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Probabilistic discovery of time series motifs

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining

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Abstract

Time series clustering by k -Means algorithm still has to overcome the dilemma of choosing the initial cluster centers. In this paper, we present a new method for initializing the k -Means clustering algorithm of time series data. Our initialization method hinges on the use of time series motif information detected by a previous task in choosing k time series in the database to be the seeds. Experimental results show that our proposed clustering approach performs better than ordinary k -Means in terms of clustering quality, robustness and running time.