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Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning

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Abstract

Time series clustering is widely applied in various areas. Existing researches focus mainly on distance measures between two time series, such as dynamic time warping (DTW) based methods, edit-distance based methods, and shapelets-based methods. In this work, we experimentally demonstrate, for the first time, that no single distance measure performs significantly better than others on clustering datasets of time series where spectral clustering is used. As such, a question arises as to how to choose an appropriate measure for a given dataset of time series. To answer this question, we propose an integration scheme that incorporates multiple distance measures using semi-supervised clustering. Our approach is able to integrate all the measures by extracting valuable underlying information for the clustering. To the best of our knowledge, this work demonstrates for the first time that the semi-supervised clustering method based on constraints is able to enhance time series clustering by combining multiple distance measures. Having tested on clustering various time series datasets, we show that our method outperforms individual measures, as well as typical integration approaches.

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Author information

Authors and Affiliations

  1. School of Computer Science, Fudan University, Shanghai, 200433, China

    Jing Zhou, Shan-Feng Zhu & Yanchun Zhang

  2. Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, 200433, China

    Jing Zhou & Shan-Feng Zhu

  3. School of Computing and Mathematics, Charles Sturt University, Albury, NSW, 2640, Australia

    Xiaodi Huang

  4. School of Engineering and Science, Victoria University, Melbourne, Victoria, 8001, Australia

    Yanchun Zhang

  5. Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, 201203, China

    Yanchun Zhang

Authors
  1. Jing Zhou
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  2. Shan-Feng Zhu
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  3. Xiaodi Huang
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  4. Yanchun Zhang
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Corresponding author

Correspondence to Jing Zhou.

Additional information

The work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61332013, 61272110, and 61370229, and the National Key Technology Research and Development Program of China under Grant No. 2013BAH72B01.

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Zhou, J., Zhu, SF., Huang, X. et al. Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning. J. Comput. Sci. Technol. 30, 859–873 (2015). https://doi.org/10.1007/s11390-015-1565-7

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  • DOI: https://doi.org/10.1007/s11390-015-1565-7

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