A General Weighted Fuzzy Clustering Algorithm

  • Zhiqiang Bao
  • Bing Han
  • Shunjun Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)


In the field of cluster analysis, most of existing algorithms assume that each feature of the samples plays a uniform contribution for cluster analysis and they are mainly developed for the uniform distribution of sample with numerical or categorical data, which cannot effectively process the non-uniformly distribution with mixed data sets in data mining. For this purpose, we propose a new general Weighted Fuzzy Clustering Algorithm to deal with the mixed data including different sample distributions and different features, in which the idea of the probability density of samples is used to assign the weights to each sample and the ReliefF algorithms is applied to give the weights to each feature. By weighting the samples and their features, the fuzzy c-means, fuzzy c-modes, fuzzy c-prototype and sample-weighted algorithms can be unified into a general framework. The experimental results with various test data sets illustrate the effectiveness of the proposed clustering algorithm.


Mixed Data Fuzzy Cluster Algorithm Cluster Prototype Weighted Cluster Algorithm ReliefF Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Qing, H.: Advance of the theory and application of fuzzy clustering analysis. Fuzzy System and Fuzzy Mathematics 12(2), 89–94 (1998) (in Chinese)Google Scholar
  2. 2.
    Gao, X.: Optimization and Applications Research on Fuzzy Clustering Algorithms, Doc-toral Thesis, Xidian University, Xi’an 710071, China (1999)Google Scholar
  3. 3.
    Huang, Z., Ng, M.K.: A fuzzy k-modes algorithm for clustering categorical data. IEEE Trans. on Fuzzy Systems 7(4), 446–452 (1999)CrossRefGoogle Scholar
  4. 4.
    Huang, Z.: A fast clustering algorithm to cluster very large categorical data sets in data Mining. In: Proceedings of the SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Dept. of Computer Science, The University of British Columbia, Canada, pp. 1–8Google Scholar
  5. 5.
    Kononenko, I.: Estimating attributes: Analysis and extensions of Relief. In: Proceedings of the 7th European Conference on Machine Learning, pp. 171–182. Springer, Heidelberg (1994)Google Scholar
  6. 6.
    Li, J., Gao, X., Jiao, L.: A new feature weighted fuzzy clustering algorithm, RSFDGr 2005 (2005)Google Scholar
  7. 7.
    Everitt, B.: Cluster Analysis. Heinemann Educational Books, New York, pp. 45–60 (1974)Google Scholar
  8. 8.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Object Function Algorithms, Plenum, New York (1981)Google Scholar
  9. 9.
    Duda, R.O., Hart, P.E.: Pattern classification and scene analysis, New York (1973)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhiqiang Bao
    • 1
  • Bing Han
    • 2
  • Shunjun Wu
    • 1
  1. 1.National Lab of Radar Signal ProcessingXidian Univ.Xi’anChina
  2. 2.School of Electronic EngineeringXidian Univ.Xi’anChina

Personalised recommendations