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Clustering by Integrating Multi-objective Optimization with Weighted K-Means and Validity Analysis

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Intelligent Data Engineering and Automated Learning – IDEAL 2006 (IDEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4224))

Abstract

This paper presents a clustering approach that integrates multi-objective optimization, weighted k-means and validity analysis in an iterative process to automatically estimate the number of clusters, and then partition the whole given data to produce the most natural clustering. The proposed approach has been tested on real-life dataset; results of both weighted and unweighed k-means are reported to demonstrate applicability and effectiveness of the proposed approach.

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References

  1. Hartigan, J.: Clustering algorithms. John Wiley and Sons, New York (1975)

    MATH  Google Scholar 

  2. Desarbo, W., Carroll, J., Clark, L., Green, P.: Synthesized clustering: A method for amalgamating clustering bases with differential weighting variables. Psychometrika 49, 57–78 (1984)

    Article  MATH  MathSciNet  Google Scholar 

  3. Modha, D., Spangler, W.: Feature weighting in k-means clustering. Machine Learning 52(3) (2003)

    Google Scholar 

  4. Huang, J., Ng, M., Rong, H., Li, Z.: Automated variable weighting in k-means type clustering. IEEE. PAMI 27(5), 657–668 (2005)

    Google Scholar 

  5. Friedman, J., Meulman, J.: Clustering objects on subsets of attributes. J. Royal Stat. Soc. B (2002)

    Google Scholar 

  6. Liu, Y., Özyer, T., Alhajj, R., Barker, K.: Integrating multi-objective genetic algorithm and validity analysis for locating and ranking alternative clustering. European Journal of Informatica 29(1), 33–40 (2005)

    MATH  Google Scholar 

  7. Liu, Y., Özyer, T., Alhajj, R., Barker, K.: Cluster validity analysis of alternative solutions from multi-objective optimization. In: Proc. of SIAM DM (2005)

    Google Scholar 

  8. Özyer, T., Alhajj, R.: Achieving natural clustering by validating results of iterative evolutionary clustering approach. In: Proceedings of IEEE International Conference on Intelligent Systems (2006)

    Google Scholar 

  9. Fridyland, J., Dudoit, S.: A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biology 3(7) (2002)

    Google Scholar 

  10. Fowlkes, E., Mallows, C.: A method for comparing two hierarchical clusterings. Journal of American Statistical Association (78), 553–569 (1983)

    Google Scholar 

  11. Wall, M.: GAlib Documentation. Massachusetts Institute of Technology (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, University of Calgary, Calgary, Alberta, Canada

    Tansel Özyer, Reda Alhajj & Ken Barker

  2. Dept. of Computer Science, Global University, Beirut, Lebanon

    Reda Alhajj

  3. Dept. of Comp. Eng., TOBB Economics & Technology University, Ankara, Turkey

    Tansel Özyer

Authors
  1. Tansel Özyer
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  2. Reda Alhajj
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  3. Ken Barker
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Editor information

Editors and Affiliations

  1. Escuela Politécnica Superior, GICAP Research Group, Universidad de Burgo, Calle Francisco de Vitoria S/N, Edifico C, Campus Vena, 09006, Burgos, Spain

    Emilio Corchado

  2. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  3. Department of Information Systems and Computation, Technical University of Valencia, Camino de Vera, Valencia, Spain

    Vicente Botti

  4. University of West Scotland, PA1 2BE, Paisley, Scotland

    Colin Fyfe

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© 2006 Springer-Verlag Berlin Heidelberg

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Özyer, T., Alhajj, R., Barker, K. (2006). Clustering by Integrating Multi-objective Optimization with Weighted K-Means and Validity Analysis. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_55

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  • DOI: https://doi.org/10.1007/11875581_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45485-4

  • Online ISBN: 978-3-540-45487-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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