Abstract
Conventional model-based clustering algorithms for time series data are limited in improving the clustering performance and also their computation complexity is high. In order to tackle this problem, a new model-based clustering algorithm with a certainty factor is proposed to evaluate the certainty degree of time series data being in a cluster. The new algorithm can be used to show a reasonable result for time series data clustering and reduce the computation complexity greatly. Performance of the algorithm is verified by the experiments on both synthetic data and real data.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zeng, J., Guo, D. (2006). A New Clustering Algorithm for Time Series Analysis. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Control and Automation. Lecture Notes in Control and Information Sciences, vol 344. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37256-1_93
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DOI: https://doi.org/10.1007/978-3-540-37256-1_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37255-4
Online ISBN: 978-3-540-37256-1
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