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Validation of Fuzzy Partitions Obtained through Fuzzy C-Means Clustering

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Foundations of Intelligent Systems (ISMIS 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2871))

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Abstract

A new cluster validity index is proposed to determine the optimal number of clusters for fuzzy partitions obtained from the fuzzy c-means algorithm. The proposed validity index exploits an overlap measure and a separation measure between clusters. A good fuzzy partition is expected to have a low degree of overlap and a larger separation distance. Testing of the proposed index on well-known data sets showed its superior effectiveness and reliability in comparison to other indexes.

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References

  1. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)

    MATH  Google Scholar 

  2. Bezdek, J.C., et al.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academy Publishers, Boston (1999)

    MATH  Google Scholar 

  3. Bezdek, J.C.: Numerical taxonomy with fuzzy sets. J. Math. Biology 1, 57–71 (1974)

    Article  MATH  MathSciNet  Google Scholar 

  4. Bezdek, J.C.: Cluster validity with fuzzy stes. J. Cybernit. 3, 58–72 (1974)

    Article  MathSciNet  Google Scholar 

  5. Pal, N.R., Bezdek, J.C.: On cluster validity for the fuzzy c-means model. IEEE Trans. on Fuzzy Systems 3, 370–379 (1995)

    Article  Google Scholar 

  6. Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence 13, 841–847 (1991)

    Article  Google Scholar 

  7. Fukuyama, Y., Sugeno, M.: A new method of choosing the number of clusters for the fuzzy c-means method. In: Proceedings of 5th Fuzzy Systems Symposium, pp. 247–250 (1989)

    Google Scholar 

  8. Kwon, S.H.: Cluster validity index for fuzzy clustering. Electronics Letters 34, 2176–2177 (1998)

    Article  Google Scholar 

  9. Rezaee, M.R., Lelieveldt, B.P.F., Reiber, J.H.C.: A new cluster validity index for the fuzzy c-mean. Pattern Recognition Letters 19, 237–246 (1998)

    Article  MATH  Google Scholar 

  10. Boudraa, A.O.: Dynamic estimation of number of clusters in data sets. Electronics Letters 35, 1606–1607 (1999)

    Article  Google Scholar 

  11. Bensaid, A.M., et al.: Validity-guided (re)clustering with applications to image segmentation. IEEE Trans. on Fuzzy Systems 4, 112–123 (1996)

    Article  Google Scholar 

  12. Bezdek, J.C., Pal, N.R.: Some new indexes of cluster validity. IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics 28, 301–315 (1998)

    Article  Google Scholar 

  13. Lee-Kwang, H., Song, Y.S., Lee, K.M.: Similarity measure between fuzzy sets and between elements. Fuzzy Sets and Systems 62, 291–293 (1994)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, KAIST, 305-701, Yusung-gu, Daejon, Korea

    Dae-Won Kim & Kwang H. Lee

  2. Department of BioSystems, KAIST, 305-701, Yusung-gu, Daejon, Korea

    Kwang H. Lee

Authors
  1. Dae-Won Kim
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  2. Kwang H. Lee
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Editor information

Editors and Affiliations

  1. The International WIC Institute, Beijing University of Technology, China

    Ning Zhong

  2. Department of Computer Science, University of North Carolina, NC 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Shimane University, 89-1 Enya-cho Izumo, 6938501, Shimane, Japan

    Shusaku Tsumoto

  4. Department of Informatics, Graduate School of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi, 819-0395, Fukuoka, Japan

    Einoshin Suzuki

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© 2003 Springer-Verlag Berlin Heidelberg

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Kim, DW., Lee, K.H. (2003). Validation of Fuzzy Partitions Obtained through Fuzzy C-Means Clustering. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds) Foundations of Intelligent Systems. ISMIS 2003. Lecture Notes in Computer Science(), vol 2871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39592-8_59

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  • DOI: https://doi.org/10.1007/978-3-540-39592-8_59

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20256-1

  • Online ISBN: 978-3-540-39592-8

  • eBook Packages: Springer Book Archive

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