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.
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Phu, L., Anh, D.T. (2011). Motif-Based Method for Initialization the K-Means Clustering for Time Series Data. In: Wang, D., Reynolds, M. (eds) AI 2011: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science(), vol 7106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25832-9_2
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DOI: https://doi.org/10.1007/978-3-642-25832-9_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25831-2
Online ISBN: 978-3-642-25832-9
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