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Generalized Agglomerative Fuzzy Clustering

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Neural Information Processing (ICONIP 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7665))

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Abstract

Fuzzy Cluster is a powerful for cluster analysis. However, inappropriate parameters selection leads Fuzzy Clustering to produce unreliable results. In addition, Fuzzy Clustering is sensitive to initialization and could be struck in local minima. Although, clustering results are validated by Cluster Validity Index but these methods obtain the best clustering result by reproduce clustering with various parameters and it is computation expensive. In order to overcome these issues, Generalized Agglomerative Fuzzy Clustering is proposed in this paper. Our proposed method is capable to find the optimum number of clusters and fuzzifier during the clustering execution. Moreover, this method is applicable to Fuzzy Clustering and its variants. Comprehensive experiments show that our agglomerative method obtained the right number of clusters and fuzzifier.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Kasetsart University, 50 Phahon Yothin Rd, Chatuchak, Bangkok, Thailand

    Kiatichai Treerattanapitak & Chuleerat Jaruskulchai

Authors
  1. Kiatichai Treerattanapitak
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  2. Chuleerat Jaruskulchai
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Editor information

Editors and Affiliations

  1. Texas A&M University at Qatar, Education City, P.O. Box 23874, Doha, Qatar

    Tingwen Huang

  2. Department of Control Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, 430074, Wuhan, Hubei, China

    Zhigang Zeng

  3. College of Computer Science, Chongqing University, 174 Shazhengjie Street, 400044, Chongqing, China

    Chuandong Li

  4. Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, China

    Chi Sing Leung

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© 2012 Springer-Verlag Berlin Heidelberg

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Treerattanapitak, K., Jaruskulchai, C. (2012). Generalized Agglomerative Fuzzy Clustering. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_5

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  • DOI: https://doi.org/10.1007/978-3-642-34487-9_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34486-2

  • Online ISBN: 978-3-642-34487-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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