Evolutionary Approaches for Cluster Analysis

  • Sandra Paterlini
  • Tommaso Minerva
Part of the Advances in Soft Computing book series (AINSC, volume 18)


The determination of the number of groups in a dataset, their composition and the most relevant measurements to be considered in clustering the data, is a high-demanding task, especially when the a priori information on the dataset is limited. Three different genetic approaches are introduced in this paper as tools for automatic data clustering and features selection. They differ in the adopted codification of the grouping problem, not in the evolutionary operator and parameters. Two of them deals with the grouping problem in a deterministic framework. The first directly approaches the grouping problem as a combinatorial one. The second tries to determine some relevant points in the data domain to be used in clustering data as group separators. A probabilistic framework is then introduced with the third one, which starts specifying the statistical model from which data are assumed to be drawn. The evolutionary approaches are, finally, compared with respect to classical partitional clustering algorithms on simulated data and on Fisher’s Iris dataset used as a benchmark.


Genetic Algorithm Fitness Function Expectation Maximization Simulated Dataset Cluster Problem 


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  1. Bandyopadhyay S., Murthy C.A., Pal S.K., “Pattern classification using genetic algorithm- Determination of H”, Pattern Recognition Letters 19, 1171–1181, 1998.MATHCrossRefGoogle Scholar
  2. Banfield J.D:, Raftery A.E., “Model-Based Gaussian and Non-Gaussian Clustering”, Biometrics 49, 803–821, September 1993.Google Scholar
  3. Baragona R., Calzini C., Battaglia F., “Genetic algorithms and clustering: an application to Fisher’s iris data”, Advances in Classification and Data Analysis, Springer, pp. 65–68, 1999.Google Scholar
  4. Bock H.H., Probabilistic models in cluster analysis, Computational Statistics Data Analysis 23, pp. 5–28, 1996.MATHCrossRefGoogle Scholar
  5. Calinski T., Harabasz J, Harabasz J., “A dendrite method for cluster analysis”, Communication in Statistics, 3(1), pp.l-27, 1974.Google Scholar
  6. Forgy E.W., “Cluster Analysis of Multivariate Data: Efficiency versus Interpretability of classification”, Biometrics, 21, 768–769, 1965.Google Scholar
  7. Fraley C., Raftery A.E., “MCLUST:software for model-based cluster and discriminant analysis”, Journal of Classification, 16, 297–306, 1999.MATHCrossRefGoogle Scholar
  8. Friedman H.P. and Rubin J., “On some invariant criterion for grouping data”, Journal of the American Statistical Association 63, 1159–1178, 1967.CrossRefMathSciNetGoogle Scholar
  9. Kim Y., Street W.N., and Menczer F. Feature selection in unsupervised learning via evolutionary search, in Proc. of the 661 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 365–369, 2000.Google Scholar
  10. Holland J.H., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Harbor, 1975.Google Scholar
  11. Le T.V., Fuzzy Evolutionary Clustering,Proceedings of the In.l Conference on Evolutionary Computation, Perth, Nov. 29, 753–758, 1995.Google Scholar
  12. Liu G.L., Introduction to combinatorial mathematics,McGraw Hill, 1968.Google Scholar
  13. Marais J., Versini G., van Wyk C.J., Rapp A., Effect of region on free and bound monoterpene and C13 nonrisoprenoid concentration in Weisser Riesling wines, South African Journal of Enology and Viticulture, 13, 71–77, 1992.Google Scholar
  14. Marriott F.H.C., “Optimization methods of cluster analysis”, Biometrics, 69, 2, pp. 417–422, 1982.CrossRefMathSciNetGoogle Scholar
  15. Paterlini S., Favaro S., Minerva T., Genetic Approaches for Data Clustering, Book of Short Papers, CLADAG2001, Palermo 7–8 July, 2001.Google Scholar
  16. Raghavan V.V., Birchand K, Birchand K., “ A clustering strategy based on a formalism of the reproductive process in a natural system”, in Proceedings of the Second International Conference on Information Storage and Retrieval, 10–22, 1979.Google Scholar
  17. Raymer M.L. et AAVV, “Dimensionality Reduction using Genetic Algorithms”, IEEE Transaction on Evolutionary Computation.Google Scholar
  18. Ricolfi L., HELGA Nuovi principi di analisi dei gruppi,FrancoAngeli s.r.l., Milano, Italy,1992.Google Scholar
  19. Ripley B.D. Pattern Recognition and Neural Networks,Cambridge University Press, 1996.Google Scholar
  20. Rudolph G., “Convergence analysis of canonical genetic algorithm”, IEEE Transactions on Neural Network, 5 (1): 96–101, January 1994.CrossRefGoogle Scholar
  21. Srikanth R., George R., Warsi N., Prabhu D., Petry F.E., Buckles B.P., “A variable-length genetic algorithm for clustering and classification”, Pattern Recognition Letters 16, 789–800, 16, 1995.Google Scholar
  22. Tseng Y.L. e Yang S.B., “ A genetic approach to the automatic clustering problem”, Pattern Recognition, Vol. 34 (2), pp. 415–424 (2001).MATHCrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Sandra Paterlini
    • 1
  • Tommaso Minerva
    • 1
  1. 1.Università di Modena e Reggio EmiliaDipartimento di Economia PoliticaModenaItaly

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