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A Hybrid Tabu Search Based Clustering Algorithm

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3682))

Abstract

The clustering problem under the criterion of minimum sum of squares clustering is a nonconvex program which possesses many locally optimal values, resulting that its solution often falls into these traps. In this paper, a hybrid tabu search based clustering algorithm called KT-Clustering is developed to explore the proper clustering of data sets. Based on the framework of tabu search, KT-Clustering gathers the optimization property of tabu search and the local search capability of K-means algorithm together. Moreover, mutation operation is adopted to establish the neighborhood of KT-Clustering. Its superiority over K-means algorithm, a genetic clustering algorithm and another tabu search based clustering algorithm is extensively demonstrated for experimental data sets.

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References

  1. Jain, A.K., Dubes, R.: Algorithms for clustering data. Prentice-Hall, New Jersey (1988)

    MATH  Google Scholar 

  2. Selim, S.Z., Ismail, M.A.: K-means-type algorithm: generalized convergence theorem and characterization of local optimality. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 81–87 (1984)

    Article  MATH  Google Scholar 

  3. Murthy, C.A., Chowdhury, N.: In search of optimal clusters using genetic algorithms. Pattern Recognition Letters 17, 825–832 (1996)

    Article  Google Scholar 

  4. Glover, F., Laguna, M.: Tabu search. Kluwer Academic Publishers, Boston (1997)

    MATH  Google Scholar 

  5. Al-sultan, K.S.: A tabu search approach to the clustering problem. Pattern Recognition 28, 1443–1451 (1995)

    Article  Google Scholar 

  6. Chelouah, R., Siarry, P.: A hybrid method combining continuous tabu search and Nelder. Mead simplex algorithms for the global optimization of multiminima functions. European Journal of Operational Research 161, 636–654 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  7. Schaffer, J.D., Caruana, R.A., Eshelman, L.J., Das, R.: A study of control parameters for genetic algorithms. In: Proceedings of the 3rd International Conference on Genetic Algorithms, pp. 51–60. Morgan Kaufmann, San Francisco (1989)

    Google Scholar 

  8. Kaufman, L., Rousseeuw, P.J.: inding groups in data. An introduction to cluster analysis. John Wiley & Sons, New York (1990)

    Google Scholar 

  9. Fisher, R.A.: The use of multiple measurements in taxonomic problem. Annals of Eugenics 7, 179–188 (1936)

    Google Scholar 

  10. Johnson, R.A., Wichern, D.W.: Applied multivariate statistical analysis. Prentice-Hall, New Jersey (1982)

    MATH  Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Liu, Y., Liu, Y., Wang, L., Chen, K. (2005). A Hybrid Tabu Search Based Clustering Algorithm. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552451_25

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  • DOI: https://doi.org/10.1007/11552451_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28895-4

  • Online ISBN: 978-3-540-31986-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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