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A New Similarity Measure Based Robust Possibilistic C-Means Clustering Algorithm

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Web Information Systems and Mining (WISM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6988))

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Abstract

In this paper, we focus on the development of a new similarity measure based robust possibilistic c-means clustering (RPCM) algorithm which is not sensitive to the selection of initial parameters, robust to noise and outliers, and able to automatically determine the number of clusters. The proposed algorithm is based on an objective function of PCM which can be regarded as special case of similarity based robust clustering algorithms. Several simulations, including artificial and benchmark data sets, are conducted to demonstrate the effectiveness of the proposed algorithm.

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Author information

Authors and Affiliations

  1. School of Electronic Engineering, University of Electronic Science and Technology, Chengdu, 611731, China

    Kexin Jia, Miao He & Ting Cheng

Authors
  1. Kexin Jia
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  2. Miao He
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  3. Ting Cheng
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Editor information

Editors and Affiliations

  1. Department of Computer and Inforamtion Science, University of Macau, Av. Padre Tomás Pereira, Taipa, Macau, China

    Zhiguo Gong

  2. School of Computer, Shanghai University, 200444, Shanghai, China

    Xiangfeng Luo

  3. College of Computer and Software, Taiyuan University of Technology, 030024, Taiyuan, China

    Junjie Chen

  4. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

  5. Department of Business Administration, Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan O, Hong Kong, China

    Fu Lee Wang

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© 2011 Springer-Verlag Berlin Heidelberg

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Jia, K., He, M., Cheng, T. (2011). A New Similarity Measure Based Robust Possibilistic C-Means Clustering Algorithm. In: Gong, Z., Luo, X., Chen, J., Lei, J., Wang, F.L. (eds) Web Information Systems and Mining. WISM 2011. Lecture Notes in Computer Science, vol 6988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23982-3_41

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  • DOI: https://doi.org/10.1007/978-3-642-23982-3_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23981-6

  • Online ISBN: 978-3-642-23982-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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