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Data Mining pp 257-288 | Cite as

Unsupervised Learning: Clustering

  • Krzysztof J. Cios
  • Roman W. Swiniarski
  • Witold Pedrycz
  • Lukasz A. Kurgan

Keywords

Minimum Span Tree Fuzzy Cluster Vector Quantization Unsupervised Learn Cluster Validity 
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.

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Refernces

  1. 1.
    Anderberg, M.R. 1973. Cluster Analysis for Applications, Academic PressGoogle Scholar
  2. 2.
    Babu, G.P., and Murthy, M.N. 1994. Clustering with evolutionary strategies. Pattern Recognition, 27, 321–329CrossRefGoogle Scholar
  3. 3.
    Bezdek, J.C. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum PressGoogle Scholar
  4. 4.
    Bezdek, J.C, Coray, C.R., Guderson, R., and Watson, J. 1981. Detection and characterization of cluster substructure, SIAM Journal of Applied Mathematics, 40: 339–372zbMATHCrossRefGoogle Scholar
  5. 5.
    Bezdek, J.C., Keller, J., Krishnampuram, R., and Pal, N.R. 1999. Fuzzy Models and Algorithms for Pattern Recognition and Image Processing, Kluwer Academic PublishersGoogle Scholar
  6. 6.
    Dave, R.N. 1990. Fuzzy shell clustering and application to circle detection in digital images, nternational Journal of General Systems, 16: 343–355CrossRefMathSciNetGoogle Scholar
  7. 7.
    Dave, R.N. 1991.Characterization and detection of noise in clustering. Pattern Recognition Letters, 12, 657–664CrossRefGoogle Scholar
  8. 8.
    Dave, R.N., and Bhaswan, K. 1992. Adaptive c-shells clustering and detection of ellipses, IEEE Transactions on Neural Networks, 3: 643–662CrossRefGoogle Scholar
  9. 9.
    Devijver, P.A., and Kittler, J. (Eds.). 1987. Pattern Recognition Theory and Applications, Springer-VerlagGoogle Scholar
  10. 10.
    Dubes, R. 1987. How many clusters are the best? –an experiment. Pattern Recognition, 20(6): 645–663CrossRefGoogle Scholar
  11. 11.
    Duda, R.O., Hart, P.E., and Stork, D.G. 2001. Pattern Classification, 2nd edition, John WileyGoogle Scholar
  12. 12.
    Dunn, J.C. 1974. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, Journal of Cybernetics, 3(3): 32–57MathSciNetGoogle Scholar
  13. 13.
    Frigui, H., and Krishnapuram, R. 1996. A comparison of fuzzy shell clustering methods for the detection of ellipses, IEEE Transactions on Fuzzy Systems, 4: 193–199CrossRefGoogle Scholar
  14. 14.
    Fukunaga, K. 1990. Introduction to Statistical Pattern Recognition, 2nd edition, Academic PressGoogle Scholar
  15. 15.
    Girolami, M. 2002. Mercer kernel-based clustering in feature space. IEEE Transactions on Neural Networks, 13: 780–784CrossRefGoogle Scholar
  16. 16.
    Hoppner, F., Klawonn, F., Kruse, R., and Runkler, T. 1999. Fuzzy Cluster Analysis, John WileyGoogle Scholar
  17. 17.
    Jain, A.K., Murthy, M.N., and Flynn, P.J. 1999. Data clustering: A review, ACM Computing Survey, 31: 264–323CrossRefGoogle Scholar
  18. 18.
    Jain, A.K., Duin, R.P.W., and Mao, J. 2000. Statistical Pattern recognition: a review, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1):4–37CrossRefGoogle Scholar
  19. 19.
    Jarvis, R.A., and Patrick, E.A. 1973. Clustering using a similarity measure based on shared near neighbors, IEEE Transactions on Computers, 22(11): 1025–1034CrossRefGoogle Scholar
  20. 20.
    Kaufmann, L., and Rousseeuw, P.J. 1990. Finding Groups in Data: An Introduction to Cluster Analysis, John WileyGoogle Scholar
  21. 21.
    Kersten, P.R. 1999. Fuzzy order statistics and their applications to fuzzy clustering, IEEE Transactions on Fuzzy Systems, 7: 708–712CrossRefGoogle Scholar
  22. 22.
    Klawonn, F., and Keller, A. 1998. Fuzzy clustering with evolutionary algorithms, International Journal of Intelligent Systems, 13: 975–991CrossRefGoogle Scholar
  23. 23.
    Kohonen, T. 1982. Self-organized formation of topologically correct feature maps, Biological Cybernetics, 43: 59–69zbMATHCrossRefMathSciNetGoogle Scholar
  24. 24.
    Kohonen, T. 1989. Self-organization and Associative Memory, Springer VerlagGoogle Scholar
  25. 25.
    Kohonen, T. 1995. Self-organizing Maps, Springer VerlagGoogle Scholar
  26. 26.
    Kohonen, T., Kaski, S., Lagus, K., and Honkela, T. 1996. Very large two-level SOM for the browsing of newsgroups, In: Proceedings of ICANN96, Lecture Notes in Computer Science, 1112, Springer, 269–274.Google Scholar
  27. 27.
    Krishnapuram, R., and Keller, J. 1993. A possibilistic approach to clustering, IEEE Transactions on Fuzzy Systems, 1(1993): 98–110CrossRefGoogle Scholar
  28. 28.
    Krishnapuram, R., and Keller, J. 1996. The possibilistic C-Means algorithm: insights and recommendations, IEEE Transactions on Fuzzy Systems, 4: 385–393CrossRefGoogle Scholar
  29. 29.
    Mali, K., and Mitra, S. 2002. Clustering of symbolic data and its validation, In: Pal, N.R., and Sugeno, M. (Eds.), Advances in Soft Computing – AFSS 2002, Springer Verlag, 339–344Google Scholar
  30. 30.
    Michalewicz, Z. 1992. Genetic Algorithms + Data Structures = Evolution Programs, Springer VerlagGoogle Scholar
  31. 31.
    Pedrycz, A., and Reformat, M. 2006. Hierarchical FCM in a stepwise discovery of structure in data, Soft Computing, 10: 244–256CrossRefGoogle Scholar
  32. 32.
    Roth, V., and Steinhage, V. 1999. Nonlinear discriminant analysis using kernel functions, In: Solla, S., Leen, T.K., and Muller, K.R. (Eds.), Advances in Neural Information Processing Systems, MIT Press, 568–574.Google Scholar
  33. 33.
    Sammon, J.W. Jr. 1969. A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, 5: 401–409CrossRefGoogle Scholar
  34. 34.
    Xie, X.L., and Beni, G. 1991. A validity measure for fuzzy clustering, EEE Transactions on Pattern Analysis and Machine Intelligence, 13: 841–847CrossRefGoogle Scholar
  35. 35.
    Webb, A. 2002. Statistical Pattern Recognition, 2nd edition, John WileyGoogle Scholar
  36. 36.
    Windham, M.P. 1980. Cluster validity for fuzzy clustering algorithms, Fuzzy Sets&Systems, 3: 1–9Google Scholar
  37. 37.
    Windham, M.P. 1982. Cluster validity for the fuzzy C-Means clustering algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11: 357–363CrossRefGoogle Scholar
  38. 38.
    Vapnik, V.N. 1998. Statistical Learning Theory, John WileyGoogle Scholar
  39. 39.
    Vesanto, J., and Alhoniemi, A. 2000. Clustering of the self-organizing map, IEEE Transactions on Neural Networks, 11: 586–600CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Krzysztof J. Cios
    • 1
    • 2
  • Roman W. Swiniarski
    • 3
  • Witold Pedrycz
    • 4
  • Lukasz A. Kurgan
    • 5
  1. 1.Virginia Commonwealth University Computer Science DeptRichmond
  2. 2.University of ColoradoUSA
  3. 3.Computer Science DeptSan Diego State University & Polish Academy of SciencesSan DiegoUSA
  4. 4.Electrical and Computer Engineering DeptUniversity of AlbertaEdmontonCanada
  5. 5.Electrical and Computer Engineering DeptUniversity of AlbertaEdmontonCanada

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