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Fuzzy Cluster Centers Separation Clustering Using Possibilistic Approach

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Advances in Swarm Intelligence (ICSI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6146))

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Abstract

Fuzzy c-means (FCM) clustering is based on minimizing the fuzzy within cluster scatter matrix trace but FCM neglects the between cluster scatter matrix trace that controls the distances between the class centroids. Based on the principle of cluster centers separation, fuzzy cluster centers separation (FCCS) clustering is an extended fuzzy c-means (FCM) clustering algorithm. FCCS attaches importance to both the fuzzy within cluster scatter matrix trace and the between cluster scatter matrix trace. However, FCCS has the same probabilistic constraints as FCM, and FCCS is sensitive to noises. To solve this problem, possibilistic cluster centers separation (PCCS) clustering is proposed based on possibilistic c-means (PCM) clustering and FCCS.Experimental results show that PCCS deals with noisy data better than FCCS and has better clustering accuracy than FCM and FCCS.

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Author information

Authors and Affiliations

  1. School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P.R. China

    Xiaohong Wu & Jiewen Zhao

  2. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, P.R. China

    Xiaohong Wu, Jun Sun & Haijun Fu

  3. Department of Information Engineering, ChuZhou Vocational Technology College, ChuZhou, 239000, P.R. China

    Bin Wu

Authors
  1. Xiaohong Wu
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  2. Bin Wu
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  3. Jun Sun
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  4. Haijun Fu
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  5. Jiewen Zhao
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Department of Machine Intelligence, Peking University, 100871, Beijing, China

    Ying Tan

  2. Research and Postgraduate Office, Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

  3. Department of Electrical and Computer Engineering, Engineering Drive 3, 4 Engineering Drive 3, National University of Singapore, 117576, Singapore

    Kay Chen Tan

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© 2010 Springer-Verlag Berlin Heidelberg

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Wu, X., Wu, B., Sun, J., Fu, H., Zhao, J. (2010). Fuzzy Cluster Centers Separation Clustering Using Possibilistic Approach. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13498-2_5

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  • DOI: https://doi.org/10.1007/978-3-642-13498-2_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13497-5

  • Online ISBN: 978-3-642-13498-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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