Skip to main content
SpringerLink
Log in
Book cover

Encyclopedia of Machine Learning pp 332–337Cite as

Evolutionary Clustering

  • Reference work entry

Synonyms

Cluster optimization; Evolutionary grouping; Genetic clustering; Genetic grouping

Definition

Evolutionary clustering refers to the application of evolutionary algorithms (also known as genetic algorithms) to data clustering (or cluster analysis), a general class of problems in machine learning, with numerous applications throughout science and industry. Different definitions of data clustering exist, but it generally concerns the identification of homogeneous groups of data (clusters) within a given data set. That is, data items that are similar to each other should be grouped together in the same cluster or group, while (usually) dissimilar items should be placed in separate clusters. The output of any clustering method is therefore a specific collection of clusters. If we have a specific way to evaluate (calculate the quality of) a given grouping into clusters, then we can consider the clustering task as an optimization problem. In general, this optimization problem is NP...

This is a preview of subscription content, log in via an institution.

Recommended Reading

  • Cole, R. M. (1998). Clustering with genetic algorithms. Masters dissertation, Department of Computer Science, University of Western Australia.

    Google Scholar 

  • Corne, D. W., Jerram, N. R., Knowles, J. D., & Oates, M. J. (2001). PESA-II: Region-based selection in evolutionary multiobjective optimization. In Proceedings of the GECCO (pp. 283–290).

    Google Scholar 

  • Delattre, M., & Hansen, P. (1980). Bicriterion cluster analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2(4), 277–291.

    Article  MATH  Google Scholar 

  • Falkenauer, E. (1998). Genetic algorithms and grouping problems. New York: Wiley.

    Google Scholar 

  • Handl, J., & Knowles, J. (2007). An evolutionary approach to multiobjective clustering. IEEE Transactions on Evolutionary Computation, 11(1), 56–76.

    Article  Google Scholar 

  • Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys, 31(3), 264–323.

    Article  Google Scholar 

  • Jones, D. R., & Beltramo, M. A. (1991). Solving partitioning problems with genetic algorithms. In R. K. Belew & L. B. Booker (Eds.), Proceedings of the fourth international conference on genetic algorithms (pp. 442–449). Morgan Kaufmann.

    Google Scholar 

  • Park, Y.-J., & Song, M.-S. (1998). A genetic algorithm for clustering problems. In Proceedings of the third annual conference on genetic programming (pp. 568–575). Morgan Kaufman.

    Google Scholar 

  • Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a dataset via the Gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411–423.

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

    Authors
    1. David Corne
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Julia Handl
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Joshua Knowles
      View author publications

      You can also search for this author in PubMed Google Scholar

    Editor information

    Editors and Affiliations

    1. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia, 2052

      Claude Sammut

    2. Faculty of Information Technology, Clayton School of Information Technology, Monash University, P.O. Box 63, Victoria, Australia, 3800

      Geoffrey I. Webb

    Rights and permissions

    Reprints and permissions

    Copyright information

    © 2011 Springer Science+Business Media, LLC

    About this entry

    Cite this entry

    Corne, D., Handl, J., Knowles, J. (2011). Evolutionary Clustering. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_271

    Download citation

    Publish with us

    Policies and ethics

    Search

    Navigation