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UK - Means Clustering for Uncertain Time Series Based on ULDTW Distance

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Intelligent Data Engineering and Automated Learning – IDEAL 2017 (IDEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

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Abstract

The probability density function represents the uncertainty of time series at each time point. In this paper, based on probability density function, we adopt the ULDTW distance for uncertain time series and apply it to the traditional UK-Means clustering. Combining the property that ULDTW distance has a one-to-many correspondence between time points in the matching process, we propose a 1ToNCenter calculation method replacing the traditional mean cluster-center calculation method to improve the accuracy of clustering results. Experiments show that the Adjusted Rand Index (ARI) of UKMeansULDTW clustering results have an obviously higher accuracy than the existing UK-Means algorithms in the high dimensional uncertain time series cases.

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Acknowledgment

This work was supported in part by the National Natural Science Foundation of China (61370075).

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Xiaoping Zhu, Zongmin Ma & Qijie Tang

Authors
  1. Xiaoping Zhu
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  2. Zongmin Ma
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  3. Qijie Tang
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Corresponding author

Correspondence to Xiaoping Zhu .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

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Zhu, X., Ma, Z., Tang, Q. (2017). UK - Means Clustering for Uncertain Time Series Based on ULDTW Distance. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-68935-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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