Skip to main content
SpringerLink
Log in

Metaheuristic Pattern Clustering – An Overview

  • Chapter
Metaheuristic Clustering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 178))

Abstract

This chapter provides a comprehensive overview to the data clustering techniques, based on naturally-inspired metaheuristic algorithms. At first the clustering problem, similarity and dissimilarity measures between patterns and the methods of cluster validation are presented in a formal way. A few classical clustering algorithms are also addressed. The chapter then discusses the relevance of population-based approach with a focus on evolutionary computing in pattern clustering and outlines the most promising evolutionary clustering methods. The chapter ends with a discussion on the automatic clustering problem, which remains largely unsolved by most of the traditional clustering algorithms.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. John Wiley and Sons, Chichester (1973)

    MATH  Google Scholar 

  2. Everitt, B.S.: Cluster Analysis, 3rd edn. Halsted Press (1993)

    Google Scholar 

  3. Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)

    MATH  Google Scholar 

  4. Arabie, P., Hubert, L.J., De Soete, G. (eds.): Clustering and Classification, River Edge. World Scientific Publishing, Singapore (1996)

    Google Scholar 

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

    Article  Google Scholar 

  6. Forgy, E.W.: Cluster Analysis of Multivariate Data: Efficiency Versus Interpretability of classification. Biometrics 21, 768–769 (1965)

    Google Scholar 

  7. Zahn, C.T.: Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Transactions on Computers C-20, 68–86 (1971)

    Article  MATH  Google Scholar 

  8. Mitchell, T.: Machine Learning. McGraw-Hill, Inc., New York (1997)

    MATH  Google Scholar 

  9. Mao, J., Jain, A.K.: Artificial neural networks for feature extraction and multivariate data projection. IEEE Trans. Neural Network 6, 296–317 (1995)

    Article  Google Scholar 

  10. Pal, N.R., Bezdek, J.C., Tsao, E.C.-K.: Generalized clustering networks and Kohonen’s self-organizing scheme. IEEE Trans. Neural Networks 4, 549–557 (1993)

    Article  Google Scholar 

  11. Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (1995)

    Google Scholar 

  12. Falkenauer, E.: Genetic Algorithms and Grouping Problems. John Wiley and Son, Chichester (1998)

    Google Scholar 

  13. Paterlini, S., Minerva, T.: Evolutionary approaches for bluster analysis. In: Bonarini, A., Masulli, F., Pasi, G. (eds.) Soft Computing Applications, pp. 167–178. Springer, Berlin (2003)

    Google Scholar 

  14. Brucker, P.: On the complexity of clustering problems. In: Beckmenn, M., Kunzi, H.P. (eds.) Optimization and Operations Research. Lecture Notes in Economics and Mathematical Systems, vol. 157, pp. 45–54. Springer, Berlin (1978)

    Google Scholar 

  15. Hamerly, G., Elkan, C.: Learning the k in k-means. In: Proceedings of the Seventeenth Annual Conference on Neural Information Processing Systems (NIPS), pp. 281–288 (December 2003)

    Google Scholar 

  16. Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 2nd edn. Elsevier Academic Press, Amsterdam (2003)

    Google Scholar 

  17. Halkidi, M., Vazirgiannis, M.: Clustering validity assessment: Finding the optimal partitioning of a data set. In: Proceedings of the 2001 IEEE International Conference on Data Mining (ICDM 2001), San Jose, California, USA, pp. 187–194 (2001)

    Google Scholar 

  18. Dunn, J.C.: Well Separated Clusters and Optimal Fuzzy Partitions. J. Cybern. 4, 95–104 (1974)

    Article  MathSciNet  Google Scholar 

  19. Calinski, R.B., Harabasz, J.: Adendrite Method for Cluster Analysis. Commun. Statistics, 1–27 (1974)

    Google Scholar 

  20. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1, 224–227 (1979)

    Article  Google Scholar 

  21. Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: Validity index for crisp and fuzzy clusters. Pattern Recognition Letters 37, 487–501 (2004)

    MATH  Google Scholar 

  22. Chou, C.H., Su, M.C., Lai, E.: A new cluster validity measure and its application to image compression. Pattern Analysis and Applications 7(2), 205–220 (2004)

    Article  MathSciNet  Google Scholar 

  23. Bezdek, J.C.: Numerical taxonomy with fuzzy sets. Journal of Math. Biol., 157–171 (1974)

    Google Scholar 

  24. Bezdek, J.C.: Cluster validity with fuzzy sets. Journal of Cybernetics (3), 58–72 (1974)

    Google Scholar 

  25. Xie, X., Beni, G.: Validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Machine Learning 3, 841–846 (1991)

    Article  Google Scholar 

  26. Pal, N.R., Biswas, J.: Cluster validation using graph theoretic concepts. Pattern Recognition 30(6), 847–857 (1997)

    Article  Google Scholar 

  27. Su, M.-C., Chou, C.-H., Lai, E.: A new cluster validity measure for clusters with different densities. In: IASTED International Conference on Intelligent Systems & Control, Salzburg, Austria, pp. 276–281 (2003)

    Google Scholar 

  28. Su, M.-C., Chou, C.-H.: A competitive learning algorithm using symmetry. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E82-A(4), 680–687 (1999)

    Google Scholar 

  29. Su, M.-C., Liu, Y.-C.: A new approach to clustering data with arbitrary shapes. Pattern Recognition 38, 1887–1901 (2005)

    Article  MATH  Google Scholar 

  30. Su, M.-C., Chou, C.-H.: A modified version of the K-means algorithm with a distance based on cluster symmetry. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(6), 674–680 (2001)

    Article  Google Scholar 

  31. Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification. Fuzzy Sets and Systems 155, 191–214 (2005)

    Article  MathSciNet  Google Scholar 

  32. Chou, C.H., Su, M.C., Lai, E.: A new cluster validity measure and its application to image compression. Pattern Analysis and Applications 7(2), 205–220 (2004)

    Article  MathSciNet  Google Scholar 

  33. Zhang, T., Ramakrishnman, R., Linvy, M.: BIRCH: An efficient method for very large databases. In: ACM SIGMOD, Montreal, Canada (1996)

    Google Scholar 

  34. Guha, S., Rastogi, R., Shim, K.: CURE: An efficient clustering algorithm for large databases. In: Proceedings of the ACM SIGMOD Conference (1998)

    Google Scholar 

  35. Guha, S., Rastogi, R., Shim, K.: ROCK: A robust clustering algorithm for categorical attributes. In: Proceedings of the IEEE Conference on Data Engineering (1999)

    Google Scholar 

  36. Hammerly, G., Elkan, C.: Alternatives to the k-means algorithm that find better clusterings. In: Proc. ACM on Information and Knowledge Management, pp. 600–607 (November 2002)

    Google Scholar 

  37. Zhang, T.: Convergence of large margin separable linear classification. In: NIPS 2000, pp. 357–363 (2001)

    Google Scholar 

  38. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkely Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)

    Google Scholar 

  39. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)

    Google Scholar 

  40. Huang, Z.: A Fast Clustering Algorithm to Cluster very Large Categorical Data Sets in Data Mining. DMKD (1997)

    Google Scholar 

  41. Ng, R., Han, J.: Efficient and effective clustering methods for spatial data mining. In: Proceeding’s of the 20th VLDB Conference, Santiago, Chile (1994)

    Google Scholar 

  42. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms, New York, Plenum (1981)

    Google Scholar 

  43. Wang, X., Wang, Y., Wang, L.: Improving fuzzy c-means clustering based on feature-weight learning. Pattern Recognition Letters 25, 1123–1132 (2004)

    Article  Google Scholar 

  44. Krishnapuram, R., Keller, J.: The possibilistic c-means algorithm: insights and recommendations. IEEE Trans. on Fuzzy Systems 4, 385–393 (1996)

    Article  Google Scholar 

  45. Bezdek, J.C., Keller, J., Krishnampuram, R., Pal, N.R.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic Publishers, Dordercht (1999)

    MATH  Google Scholar 

  46. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39(1), 1–38 (1977)

    MATH  MathSciNet  Google Scholar 

  47. McLachlan, G., Krishnan, T.: The EM algorithm and extensions. Wiley series in probability and statistics. John Wiley & Sons, Chichester (1997)

    MATH  Google Scholar 

  48. Hamerly, G., Elkan, C.: Alternatives to the k-Means algorithm that find better clustering (pdf). In: Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM 2002), pp. 600–607 (November 2002)

    Google Scholar 

  49. Veenman, C.J., Reinders, M.J.T., Backer, E.: A maximum variance cluster algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(9), 1273–1280 (2002)

    Article  Google Scholar 

  50. Alldrin, N., Smith, A., Turnbull, D.: Clustering with EM and K-means, unpublished Manuscript (2003), http://louis.ucsd.edu/~nalldrin/research/cse253\_wi03.pdf

  51. Turi, R.H.: Clustering-based colour image segmentation. PhD Thesis. Monash University, Australia (2001)

    Google Scholar 

  52. Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Proceedings of eural Information Processing Systems (NIPS 2001) (2001)

    Google Scholar 

  53. Zhang, B., Hsu, M., Dayal, U.: K-Harmonic means - A data clustering algorithm. Technical Report HPL-1999-124. Hewlett-Packard Labs (1999)

    Google Scholar 

  54. Zhang, B.: Generalized K-Harmonic means - boosting in unsupervised learning. Technical Report HPL-2000-137. Hewlett-Packard Labs (2000)

    Google Scholar 

  55. Bradley, P.S., Fayyad, U.M.: Refining Initial Points for K-Means Clustering. In: ICML 1998, pp. 91–99 (1998)

    Google Scholar 

  56. Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceeding of 2nd Int. Conf. on Knowledge Discovery and Data Mining, Portland, pp. 226–23 (1996)

    Google Scholar 

  57. Hinneburg, A., Keim, D.: An efficient approach to clustering in large multimedia databases with noise. In: Proceedings of KDD Conference (1998)

    Google Scholar 

  58. Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)

    Article  Google Scholar 

  59. Wang, W., Yang, J., Muntz, R.: STING: A statistical Information grid approach to spatial data mining. In: Proceedings of 23rd VLDB Conference (1997)

    Google Scholar 

  60. Sheikholeslami, C., Chatterjee, S., Zhang, A.: WaveCluster: A-multi resolution clustering approach for very large spatial database. In: Proceedings of 24th VLDB Conference, New York, USA (1998)

    Google Scholar 

  61. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, USA (2001)

    Google Scholar 

  62. Back, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. Oxford University Press, Oxford (1997)

    Google Scholar 

  63. De Jong, K.A.: Evolutionary Computation: A Unified Approach. MIT Press, Cambridge (2006)

    MATH  Google Scholar 

  64. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  65. Fogel, D.B.: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway (1995)

    Google Scholar 

  66. Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence through Simulated Evolution. John Wiley, New York (1966)

    MATH  Google Scholar 

  67. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  68. Rechenberg, I.: Evolutionsstrategie - Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution (PhD thesis, 1971); Reprinted by Fromman-Holzboog (1973)

    Google Scholar 

  69. Schwefel, H.-P.: Numerische Optimierung von Computer-Modellen (PhD thesis) (1974); Reprinted by Birkhäuser (1977)

    Google Scholar 

  70. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Evolution. MIT Press, Massachusetts (1992)

    Google Scholar 

  71. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press Inc., Oxford (1999)

    MATH  Google Scholar 

  72. Martinetz, T.M., Schulten, K.J.: A neural-gas network learns topologies. In: Kohonen, T., Mäkisara, K., Simula, O., Kangas, J. (eds.) Artificial Neural Networks, pp. 397–402. North-Holland, Amsterdam (1991)

    Google Scholar 

  73. Langton, C.G. (ed.): Artificial Life: An Overview. MIT Press, Cambridge (1995)

    Google Scholar 

  74. Kobti, Z., Reynolds, R., Kohler, T.: A multi-agent simulation using cultural algorithms: The effect of culture on the resilience of social systems. In: IEEE Congress on Evolutionary Computation, Canberra, Australia, December 5-12 (2003)

    Google Scholar 

  75. Lee, K.S., Geem, Z.W.: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Computer Methods in Applied Mechanics and Engineering (2005)

    Google Scholar 

  76. Dasgupta, D. (ed.): Artificial Immune Systems and Their Applications. Springer, Berlin (1999)

    MATH  Google Scholar 

  77. Wojtusiak, J., Michalski, R.S.: The LEM3 Implementation of learnable evolution model and Its testing on complex function optimization problems. In: Proceedings of Genetic and Evolutionary Computation Conference, GECCO 2006, Seattle, WA, July 8-12 (2006)

    Google Scholar 

  78. Michalewicz, Z., Fogel, D.B.: How to Solve It: Modern Heuristics. Springer, Heidelberg (2000)

    MATH  Google Scholar 

  79. Beyer, H.-G., Schwefel, H.-P.: Evolution Strategies: A Comprehensive Introduction. Journal Natural Computing 1(1), 3–52 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  80. Beyer, H.-G.: The Theory of Evolution Strategies. Springer, Heidelberg (2001)

    Google Scholar 

  81. Kita, H.: A comparison study of self-adaptation in volution etrategies and real-coded genetic algorithms. Evolutionary Computation 9(2), 223–241 (2001)

    Article  MathSciNet  Google Scholar 

  82. Hansen, N., Ostermeier, A.: Completely de-randomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159–195 (2001)

    Article  Google Scholar 

  83. Fogel, D.B.: Evolving Artificial Intelligence, Ph.D. dissertation, Univ. California, San Diego, CA (1992)

    Google Scholar 

  84. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  85. DeJong, K.A.: An analysis of behavior of a class of genetic adaptive systems. Doctoral Dissertation, University of Michigan (1975)

    Google Scholar 

  86. Davis, T.E., Principa, J.C.: A Markov chain framework for the simple genetic algorithm. Evolutionary Computation 1(3), 269–288 (1993)

    Article  Google Scholar 

  87. Muehlenbein, H., Chakraborty, U.K.: Gene pool recombination genetic algorithm and the onemax function. Journal of Computing and Information Technology 5(3), 167–182 (1997)

    Google Scholar 

  88. Vose, M.D., Liepins, G.E.: Punctuated equilibrium in genetic search. Complex Systems 5, 31–44 (1991)

    MATH  MathSciNet  Google Scholar 

  89. Filho, J.L.R., Treleven, P.C.: Genetic Algorithm Programming Environment, pp. 28–43. IEEE Computer Society Press, Los Alamitos (1994)

    Google Scholar 

  90. Smith, S.F.: A Learning System Based on Genetic Adaptive Algorithms, PhD dissertation (University of Pittsburgh) (1980)

    Google Scholar 

  91. Cramer, N.L.: A representation for the adaptive generation of simple sequential programs. In: John, J. (ed.) Proceedings of an International Conference on Genetic Algorithms and the Applications, Grefenstette, Carnegie Mellon University (1985)

    Google Scholar 

  92. Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic programming: An Introduction: On the Automatic Evolution of Computer Programs and Its Applications. Morgan Kaufmann, San Francisco (1998)

    MATH  Google Scholar 

  93. Nordin, J.P.: Evolutionary Program Induction of Binary Machine Code and its Application. Krehl Verlag, Muenster, Germany (1997)

    Google Scholar 

  94. Beni, G., Wang, U.: Swarm intelligence in cellular robotic systems. In: NATO Advanced Workshop on Robots and Biological Systems, Il Ciocco, Tuscany, Italy (1989)

    Google Scholar 

  95. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. of the IEEE Int. Conf. on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)

    Google Scholar 

  96. Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  97. Dorigo, M.: Optimization, Learning and Natural Algorithms, PhD thesis, Politecnico di Milano, Italy (1992)

    Google Scholar 

  98. Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics–Part B 26(1), 29–41 (1996)

    Article  Google Scholar 

  99. Dorigo, M., Gambardella, L.M.: Ant Colony System: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)

    Article  Google Scholar 

  100. Johnson, D.S., Mc Geoch, L.A.: The Traveling Salesman Problem: A Case Study in Local Optimization. In: Aarts, E.H.L., Lenstra, J.K. (eds.) Local Search in Combinatorial Optimization, pp. 215–310. John Wiley and Sons Ltd., Chichester (1997)

    Google Scholar 

  101. Abraham, A., Das, S., Roy, S.: Swarm intelligence algorithms for data clustering. In: Maimon, O., Rokach, L. (eds.) Soft Computing for Knowledge Discovery and Data Mining, pp. 279–313. Springer, Germany (2007)

    Google Scholar 

  102. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)

    Google Scholar 

  103. Raghavan, V.V., 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, pp. 10–22

    Google Scholar 

  104. Murthy, C.A., Chowdury, N.: search of optimal clusters using genetic algorithm. Pattern Recognition Letters 17, 825–832 (1996)

    Article  Google Scholar 

  105. Falkenauer, E.: Genetic Algorithms and Grouping Problems. John Wiley and Son, Chichester (1998)

    Google Scholar 

  106. Bandyopadhyay, S., Murthy, C.A., Pal, S.K.: Pattern classification with genetic algorithms. Pattern Recognition Letters 16, 801–808 (1995)

    Article  Google Scholar 

  107. Bandyopadhyay, S., Murthy, C.A., Pal, S.K.: Pattern classification using genetic algorithm: determination of H. Pattern Recognition Letters 19, 1171–1181 (1998)

    Article  MATH  Google Scholar 

  108. Bandyopadhyay, S., Murthy, C.A., Pal, S.K.: Theoretic performance of genetic pattern classifier. Journal of the Franklin Institute 336, 387–422 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  109. Bandyopadhyay, S., Maulik, U.: Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognition 35, 1197–1208 (2002)

    Article  MATH  Google Scholar 

  110. Srikanth, R., George, R., Warsi, N., Prabhu, D., Petri, F.E., Buckles, B.P.: A variable-length genetic algorithm for clustering and classification. Pattern Recognition Letters 16, 789–800 (1995)

    Article  Google Scholar 

  111. Maulik, U., Bandyopadhyay, S.: Genetic algorithm-based clustering technique. Pattern Recognition 33, 1455–1465 (2000)

    Article  Google Scholar 

  112. Chiou, Y.C., Lan, L.W.: Theory and methodology genetic clustering algorithms. European Journal of operational Research 135, 413–427 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  113. Bandyopadhyay, S., Maulik, U.: An evolutionary technique based on K-means algorithm for optimal clustering in RN. Information Sciences 146, 221–237 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  114. Paterlini, S., Minerva, T.: Evolutionary approaches for cluster analysis. In: Bonarini, A., Masulli, F., Pasi, G. (eds.) Soft Computing Applications, pp. 167–178. Springer, Berlin (2003)

    Google Scholar 

  115. Krishna, K., Murty, M.N.: Genetic K-means algorithm. IEEE Transaction on Systems, Man and Cybernetics 29 (1999)

    Google Scholar 

  116. Lee, C.-Y., Antonsson, E.K.: Dynamic partitional clustering using evolution strategies. In: Proceedings of the Third Asia Pacific Conference on Simulated Evolution and Learning (2000), http://citeseer.ist.psu.edu/501113.html

  117. Sarkar, M., Yegnanarayana, B., Khemani, D.: A clustering algorithm using an evolutionary programming-based approach. Pattern Recognition Letters 18, 975–986 (1997)

    Article  Google Scholar 

  118. Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chetien, L.: The dynamics of collective sorting: Robot-like ants and ant-like robots. In: Meyer, J.A., Wilson, S.W. (eds.) Proceedings of the First International Conference on Simulation of Adaptive Behaviour: From Animals to Animats 1, pp. 356–363. MIT Press, Cambridge (1991)

    Google Scholar 

  119. Handl, J., Knowles, J., Dorigo, M.: Ant-based clustering: a comparative study of its relative performance with respect to k-means, average link and 1D-SOM., Technical Report TR/IRIDIA/2003-24, IRIDIA, Universite Libre de Bruxelles, Belgium (2003)

    Google Scholar 

  120. Lumer, E., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: Proceedings Third International Conference on Simulation of Adaptive Behavior: from Animals to Animates 3, pp. 499–508. MIT Press, Cambridge (1994)

    Google Scholar 

  121. Lumer, E., Faieta, B.: Exploratory Database Analysis via Self-Organization, unpublished manuscript (1995)

    Google Scholar 

  122. Emergent colonization and graph partitioning. In: Proceedings of the Third International Conference on Simulation of Adaptive Behaviour: From Animals to Animats 3, pp. 494–500. MIT Press, Cambridge (1994)

    Google Scholar 

  123. Kuntz, P., Snyers, D.: New results on an ant-based heuristic for highlighting the organization of large graphs. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1451–1458. IEEE Press, Piscataway (1999)

    Chapter  Google Scholar 

  124. Kuntz, P., Snyers, D., Layzell, P.: A Stochastic Heuristic for Visualizing Graph Clusters in a bi-dimensional Space Prior to Partitioning. Journal of Heuristics 5(3), 327–351 (1998)

    Article  Google Scholar 

  125. Handl, J., Meyer, B.: Improved ant-based clustering and sorting. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, p. 913. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  126. Hoe, K.M., Lai, W.-K., Tai, T.S.Y.: Homogeneous ants for web document similarity modeling and categorization. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, p. 256. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  127. Ramos, V., Merelo, J.J.: Self-organized stigmergic document maps: Environments as a mechanism for context learning. In: Proceedings of the First Spanish Conference on Evolutionary and Bio-Inspired Algorithms (AEB 2002), pp. 284–293. Centro Univ. M´erida, Merida, Spain (2002)

    Google Scholar 

  128. Ramos, V., Muge, F., Pina, P.: Self-organized data and image retrieval as a consequence of inter-dynamic synergistic relationships in artificial ant colonies. Soft Computing Systems: Design, Management and Applications 87, 500–509 (2002)

    Google Scholar 

  129. Monmarche, N., Slimane, M., Venturini, G.: Ant class: discovery of clusters in numeric data by a hybridization of an ant colony with the k means algorithm. Internal Report No. 213, E3i, Laboratoire d’Informatique, Universite de Tours (1999)

    Google Scholar 

  130. Kanade, P.M., Hall, L.O.: Fuzzy ants as a clustering concept. In: Proceedings of the 22nd International Conference of the North American Fuzzy Information Processing Society (NAFIPS 2003), pp. 227–232 (2003)

    Google Scholar 

  131. Tsang, W., Kwong, S.: Ant colony clustering and feature extraction for anomaly intrusion detection. In: Abraham, A., Grosan, C., Ramos, V. (eds.) Swarm Intelligence in Data Mining, pp. 101–121. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  132. Omran, M., Salman, A., Engelbrecht, A.P.: Image classification using particle swarm optimization. In: Conference on Simulated Evolution and Learning, vol. 1, pp. 370–374 (2002)

    Google Scholar 

  133. Omran, M., Engelbrecht, A.P., Salman, A.: Particle swarm optimization method for image clustering. International Journal of Pattern Recognition and Artificial Intelligence 19(3), 297–322 (2005)

    Article  Google Scholar 

  134. van der Merwe, D.W., Engelbrecht, A.P.: Data clustering using particle swarm optimization. In: Proceedings of the 2003 IEEE Congress on Evolutionary Computation, Piscataway, NJ, pp. 215–220 (2003)

    Google Scholar 

  135. Xiao, X., Dow, E.R., Eberhart, R.C., Miled, Z.B., Oppelt, R.J.: Gene clustering using self-organizing maps and particle swarm optimization. In: Proc. of the 17th International Symposium on Parallel and Distributed Processing (PDPS 2003), IEEE Computer Society, Washington (2003)

    Google Scholar 

  136. Cui, X., Potok, T.E.: Document clustering analysis based on hybrid PSO + K-means algorithm. Journal of Computer Sciences (Special Issue), 27–33 (2005) ISSN 1549-3636

    Google Scholar 

  137. Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. Journal of Intelligent Information Systems (JIIS) 17(2-3), 107–145 (2001)

    Article  MATH  Google Scholar 

  138. Rosenberger, C., Chehdi, K.: Unsupervised clustering method with optimal estimation of the number of clusters: Application to image segmentation. In: Proc. IEEE International Conference on Pattern Recognition (ICPR), vol. 1, Barcelona, pp. 1656–1659 (2000)

    Google Scholar 

  139. Ball, G., Hall, D.: A clustering technique for summarizing multivariate data. Behavioral Science 12, 153–155 (1967)

    Article  Google Scholar 

  140. Huang, K.: A synergistic automatic clustering technique (Syneract) for multispectral image analysis. Photogrammetric Engineering and Remote Sensing 1(1), 33–40 (2002)

    Google Scholar 

  141. Pelleg, D., Moore, A.: X-means: Extending K-means with efficient estimation of the number of clusters. In: Proceedings of the 17th International Conference on Machine Learning, pp. 727–734. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  142. Kass, R., Wasserman, L.: A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion. Journal of the American Statistical Association 90(431), 928–934 (1995)

    Article  MATH  MathSciNet  Google Scholar 

  143. Wallace, C.S., Dowe, D.L.: Intrinsic classification by MML – the snob program. In: Proceedings 7th Australian Joint Conference on Artificial Intelligence, UNE, Armidale, NSW, Australia, pp. 37–44 (1994)

    Google Scholar 

  144. Wallace, C.S., Boulton, D.M.: An information measure for classification. The Computer Journal 11, 185–194 (1968)

    MATH  Google Scholar 

  145. Oliver, J.J., Hand, D.: Introduction to minimum encoding inference. Technical Report No. 94/205, Department of Computer Science, Monash University, Australia (1994)

    Google Scholar 

  146. Bischof, H., Leonardis, A., Selb, A.: MDL principle for robust vector quantization. Pattern Analysis and Applications 2, 59–72 (1999)

    Article  MATH  Google Scholar 

  147. Gath, I., Geva, A.: Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7), 773–781 (1989)

    Article  Google Scholar 

  148. Lorette, A., Descombes, X., Zerubia, J.: Fully unsupervised fuzzy clustering with entropy criterion. In: International Conference on Pattern Recognition (ICPR 2000), vol. 3, pp. 3998–4001 (2000)

    Google Scholar 

  149. Frigui, H., Krishnapuram, R.: A robust competitive clustering algorithm with applications in computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(5), 450–465 (1999)

    Article  Google Scholar 

  150. Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences, vol. 30. Springer, N.Y. (1995)

    Google Scholar 

  151. Vesanto, J., Alhoniemi, E.: Clustering of the self organizing map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000), http://citeseer.ist.psu.edu/vesanto00clustering.html

    Google Scholar 

  152. Ultsch, A.: Emergence in Self-organizing feature maps. In: Proceedings Workshop on Self-Organizing Maps (WSOM 2007), Bielefeld, Germany (2007)

    Google Scholar 

  153. Mehrotra, K., Mohan, C.: Rakka: Elements of Artificial Neural Networks. MIT Press, Cambridge (1997)

    Google Scholar 

  154. Pandya, A., Macy, R.: Pattern Recognition with Neural Networks in C++. CRC Press, Boca Raton (1996)

    Google Scholar 

  155. Maulik, U., Bandyopadhyay, S.: Fuzzy partitioning using real coded variable length genetic algorithm for pixel classification. IEEE Transactions on Geosciences and Remote Sensing 41(5), 1075–1081 (2003)

    Article  Google Scholar 

  156. Omran, M.G., Engelbrecht, A.P., Salman, A.: Dynamic clustering using particle swarm optimization with application in unsupervised image classification. In: Proceedings of World Academy of Science, Engineering and Technology, vol. 9 (November 2005)

    Google Scholar 

  157. Sawaragi, Y., Nakayama, H., Tanino, T.: Theory of multiobjective optimization. Mathematics in Science and Engineering, vol. 176. Academic Press Inc., Orlando (1985)

    MATH  Google Scholar 

  158. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)

    MATH  Google Scholar 

  159. Coello Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  160. Abraham, A., Jain, L.C., Goldberg, R. (eds.): Evolutionary Multiobjective Optimization: Theoretical Advances and Applications. Springer, London (2005)

    MATH  Google Scholar 

  161. Knowles, J.D., Corne, D.W.: Approxmating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation 8(2), 149–172 (2000)

    Article  Google Scholar 

  162. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2) (2002)

    Google Scholar 

  163. Minaei-Bidgoli, B., Topchy, A., Punch, W.F.: Ensembles of partitions via data resampling. In: Proc. Int. Conf. Inf. Technol. Coding Comput., pp. 188–192 (2004)

    Google Scholar 

  164. Topchy, A., Jain, A.K., Punch, W.: Clustering ensembles: Models of consensus and weak partitions. IEEE Trans. Pattern Anal. Mach. Intell. 27(12), 1866–1881 (2005)

    Article  Google Scholar 

  165. Topchy, A., Minaei, B., Jain, A.K., Punch, W.: Adaptive clustering ensembles. In: Proc. Int. Conf. Pattern Recognit., pp. 272–275 (2004)

    Google Scholar 

  166. Strehl, A., Ghosh, J.: Cluster ensembles—A knowledge reuse framework for combining multiple partitions. J. Machine Learn. Res. 3, 583–617 (2002)

    Article  MathSciNet  Google Scholar 

  167. Topchy, A., Jain, A.K., Punch, W.: A mixture model for clustering ensembles. In: Proc. SIAM Int. Conf. Data Mining, pp. 379–390 (2004)

    Google Scholar 

  168. Law, M., Topchy, A., Jain, A.K.: Multiobjective Data Clustering. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 424–430 (2004)

    Google Scholar 

  169. Bandyopadhyay, S., Maulik, U., Mukhopadhyay, A.: Multiobjective genetic clustering for pixel classification in remote sensing imagery, IEEE Transactions Geoscience and Remote Sensing (2006)

    Google Scholar 

  170. Handl, J., Knowles, J.: Evolutionary multiobjective clustering. In: Proc. 8th Int. Conf. Parallel Problem Solving from Nature, pp. 1081–1091 (2004)

    Google Scholar 

  171. Corne, D.W., Jerram, N.R., Knowles, J.D., Oates, M.J.: PESA-II: Region-based selection in evolutionary multiobjective optimization. In: Proc. Genetic Evol. Comput. Conf., pp. 283–290 (2001)

    Google Scholar 

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

    Article  Google Scholar 

  173. Handl, J., Knowles, J.: Exploiting the tradeoff—the benefits of multiple objectives in data clustering. In: Proc. 3rd Int. Conf. Evol. Multicriterion Optim., pp. 547–560 (2005)

    Google Scholar 

  174. Handl, J., Knowles, J.: Improvements to the scalability of multiobjective clustering. In: Proc. IEEE Congr. Evol. Comput., vol. 3, pp. 2372–2379 (2005)

    Google Scholar 

  175. Handl, J., Knowles, J.: Multiobjective clustering around medoids. In: Proc. IEEE Congr. Evol. Comput., vol. 1, pp. 632–639 (2005)

    Google Scholar 

  176. Storn, R., Price, K.V.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces., Technical Report TR-95-012, ICSI (1995), http://http.icsi.berkeley.edu/~storn/litera.html

  177. Storn, R., Price, K.V., Lampinen, J.: Differential Evolution - A Practical Approach to Global Optimization. Springer, Berlin (2005)

    MATH  Google Scholar 

  178. Snyman, J.A.: Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms. Springer Publishing, Heidelberg (2005)

    MATH  Google Scholar 

  179. Hahn, W.: Theory and Application of Liapunov’s Direct Method. Prentice-Hall, Englewood Cliffs (1963)

    MATH  Google Scholar 

  180. Blake, C., Keough, E., Merz, C.J.: UCI Repository of Machine Learning Database (1998), http://www.ics.uci.edu/~mlearn/MLrepository.html

  181. Muller, K.R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.: An introduction to kernel-based learning algorithms. IEEE Trans. Neural Networks 12(2), 181–202 (2001)

    Article  Google Scholar 

  182. Girolami, M.: Mercer kernel-based clustering in feature space. IEEE Trans. Neural Networks 13(3), 780–784 (2002)

    Article  Google Scholar 

  183. Scholkopf, B., Smola, A.J.: Learning with Kernels. The MIT Press, Cambridge (2002)

    Google Scholar 

Download references

Authors
  1. Swagatam Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ajith Abraham
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amit Konar
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Das, S., Abraham, A., Konar, A. (2009). Metaheuristic Pattern Clustering – An Overview. In: Metaheuristic Clustering. Studies in Computational Intelligence, vol 178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93964-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-93964-1_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-92172-1

  • Online ISBN: 978-3-540-93964-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation