Maximin Initialization for Cluster Analysis

  • Richard J. Hathaway
  • James C. Bezdek
  • Jacalyn M. Huband
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)


Most iterative clustering algorithms require a good initialization to achieve accurate results. A new initialization procedure for all such algorithms is given that is exact when the data contain compact, separated clusters. Our examples use c-means clustering.


Object Data Distinguished Object True Label Search Array Good Initialization 
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.


  1. 1.
    Bezdek, J.C., Hathaway, R.J., Huband, J.M., Leckie, C., Kotagiri, R.: Approximate Clustering in Very Large Relational Data. Inter. J. Intell. Systems (in press, 2005)Google Scholar
  2. 2.
    Hathaway, R.J., Bezdek, J.C., Huband, J.M.: Scalable Visual Assessment of Cluster Tendency for Large Data Sets. Pattern Recognition (in press, 2005)Google Scholar
  3. 3.
    Dunn, J.C.: A Fuzzy Relative of the ISODATA Process and its Use in Detecting Compact Well-separated Clusters. J. Cybernetics 3, 32–57 (1973)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Borg, I., Lingoes, J.: Multidimensional Similarity Structure Analysis. Springer, New York (1987)Google Scholar
  5. 5.
    Kendall, M., Gibbons, J.D.: Rank Correlation Methods. Oxford University Press, New York (1990)MATHGoogle Scholar
  6. 6.
    Ball, G., Hall, D.: A Clustering Technique for Summarizing Multivariate Data. Behavioral Science 12, 153–155 (1967)CrossRefGoogle Scholar
  7. 7.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)MATHGoogle Scholar
  8. 8.
    Krishnapuram, R., Keller, J.M.: A Possibilistic Approach to Clustering. IEEE Trans. on Fuzzy Systems 1, 98–110 (1993)CrossRefGoogle Scholar
  9. 9.
    Hathaway, R.J., Bezdek, J.C., Davenport, J.: On Relational Data Versions of c-Means Algorithms. Pattern Recognition Letters 17, 607–612 (1996)CrossRefGoogle Scholar
  10. 10.
    Ruspini, E.: A New Approach to Clustering. Information and Control 15, 22–32 (1969)MATHCrossRefGoogle Scholar
  11. 11.
    Bezdek, J.C., Hathaway, R.J.: Convergence of Alternating Optimization. Neural, Parallel and Scientific Computation 11(4), 351–368 (2003)MATHMathSciNetGoogle Scholar
  12. 12.
    Bezdek, J.C., Keller, J.M., Krishnapuram, R., Pal, N.R.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer, Norwell (1999)MATHGoogle Scholar
  13. 13.
    McLachlan, G., Peel, D.: Finite Mixture Models. John Wiley & Sons, New York (2000)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Richard J. Hathaway
    • 1
  • James C. Bezdek
    • 2
  • Jacalyn M. Huband
    • 2
  1. 1.Department of Mathematical SciencesGeorgia Southern UniversityStatesboroUSA
  2. 2.Computer Science DepartmentUniversity of West FloridaPensacolaUSA

Personalised recommendations