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A Fuzzy Comprehensive Clustering Method

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Advanced Data Mining and Applications (ADMA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4632))

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Abstract

Fuzzy comprehensive evaluation cannot reasonably differentiate the close membership values, e.g. 0.70 and 0.69. When the results have to be decided on the basis of maximum fuzzy membership value, some related information among similar objects may be neglected. At the same time, supervised fuzzy clustering analysis selects the threshold according to subjective experience. But different users may give different thresholds, and different thresholds may further get different clustering results. Integrating both fuzzy comprehensive evaluation and fuzzy clustering analysis in a unified way, this paper proposes a fuzzy comprehensive clustering method based on the maximum remainder algorithms and maximum characteristics algorithms. First, the principle of fuzzy comprehensive clustering is given. Based on the membership matrix of fuzzy comprehensive evaluation, fuzzy similar matrix is generated. Then a fuzzy equivalent matrix is produced from the fuzzy similar matrix. According to the fuzzy equivalent matrix, fuzzy clustering is implemented via the maximum remainder algorithms on the basis of fuzzy confidence level. And the grades of the resulting clusters are computed by using the maximum characteristics algorithms. Finally, a case study is given on land grading in Nanning city, the results of which show the proposed fuzzy comprehensive clustering method is able to overcome the disadvantages of either fuzzy comprehensive evaluation or fuzzy clustering analysis.

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Author information

Authors and Affiliations

  1. International School of Software, Wuhan University, Wuhan 430072, China

    Shuliang Wang

  2. The State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China

    Shuliang Wang & Xinzhou Wang

Authors
  1. Shuliang Wang
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  2. Xinzhou Wang
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Editor information

Editors and Affiliations

  1. Computer Science Department, University of Calgary , Calgary, AB, Canada

    Reda Alhajj

  2. School of Computer Science and Technology , Harbin Institute of Technology, Harbin, China

    Hong Gao

  3. School of Computer Science and Technology , Harbin Institute of Technology , Harbin, China

    Jianzhong Li

  4. School of Information Technology and Electronic Engineering , The University of Queensland , Queensland, Australia

    Xue Li

  5. Department of Computing Science , University of Alberta, Edmonton, AB, Canada

    Osmar R. Zaïane

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© 2007 Springer Berlin Heidelberg

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Wang, S., Wang, X. (2007). A Fuzzy Comprehensive Clustering Method. In: Alhajj, R., Gao, H., Li, J., Li, X., Zaïane, O.R. (eds) Advanced Data Mining and Applications. ADMA 2007. Lecture Notes in Computer Science(), vol 4632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73871-8_45

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  • DOI: https://doi.org/10.1007/978-3-540-73871-8_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73870-1

  • Online ISBN: 978-3-540-73871-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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