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Swarm Intelligence-Based Clustering Algorithms: A Survey

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Unsupervised Learning Algorithms

Abstract

Swarm intelligence (SI) is an artificial intelligence technique that depends on the collective properties emerging from multi-agents in a swarm. In this work, the SI-based algorithms for hard (crisp) clustering are reviewed. They are studied in five groups: particle swarm optimization, ant colony optimization, ant-based sorting, hybrid algorithms, and other SI-based algorithms. Agents are the key elements of the SI-based algorithms, as they determine how the solutions are generated and directly affect the exploration and exploitation capabilities of the search procedure. Hence, a new classification scheme is proposed for the SI-based clustering algorithms according to the agent representation. We elaborate on which representation schemes are used in different algorithm categories. We also examine how the SI-based algorithms, together with the representation schemes, address the challenging characteristics of the clustering problem such as multiple objectives, unknown number of clusters, arbitrary-shaped clusters, data types, constraints, and scalability. The pros and cons of each representation scheme are discussed. Finally, future research directions are suggested.

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References

  1. 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, New York (2008)

    Chapter  Google Scholar 

  2. Alam, S., Dobbie, G., Koh, Y.S., Riddle, P., Rehman, S.U.: Research on particle swarm optimization based clustering: a systematic review of literature and techniques. Swarm Evol. Comput. 17, 1–13 (2014)

    Article  Google Scholar 

  3. Alani, H., Jones, C.B., Tudhope, D.: Voronoi-based region approximation for geographical information retrieval with gazetteers. Int. J. Geogr. Inf. Sci. 15(4), 287–306 (2001)

    Article  MATH  Google Scholar 

  4. Azzag, H., Venturini, G., Oliver, A., Guinot, C.: A hierarchical ant-based clustering algorithm and its use in three real-world applications. Eur. J. Oper. Res. 179(3), 906–922 (2007)

    Article  MATH  Google Scholar 

  5. Bandyopadhyay, S., Saha, S.: GAPS: A clustering method using a new point symmetry-based distance measure. Pattern Recogn. 40, 3430–3451 (2007)

    Article  MATH  Google Scholar 

  6. Bandyopadhyay, S., Saha, S.: A point symmetry-based clustering technique for automatic evolution of clusters. IEEE Trans. Knowl. Data Eng. 20(11), 1441–1457 (2008)

    Article  Google Scholar 

  7. Basu, S., Davidson, I.: KDD 2006 Tutorial Clustering with Constraints: Theory and Practice (2006). Available via http://www.ai.sri.com/~basu/kdd-tutorial-2006.Cited2March2015

  8. Bong, C.-W., Rajeswari, M.: Multi-objective nature-inspired clustering and classification techniques for image segmentation. Appl. Soft Comput. 11(4), 3271–3282 (2011)

    Article  Google Scholar 

  9. Boryczka, U.: Finding groups in data: Cluster analysis with ants. Appl. Soft Comput. 9(1), 61–70 (2009)

    Article  Google Scholar 

  10. Celebi, M.E.: Partitional Clustering Algorithms. Springer, Switzerland (2015)

    Book  MATH  Google Scholar 

  11. Chen, A., Chen, C.: A new efficient approach for data clustering in electronic library using ant colony clustering algorithm. Electron. Libr. 24(4), 548–559 (2006)

    Article  Google Scholar 

  12. Cura, T.: A particle swarm optimization approach to clustering. Expert Syst. Appl. 39(1), 1582–1588 (2012)

    Article  Google Scholar 

  13. D’Urso, P., De Giovanni, L., Disegna, M., Massari, R.: Bagged clustering and its application to tourism market segmentation. Expert Syst. Appl. 40(12), 4944–4956 (2013)

    Article  Google Scholar 

  14. Das, S., Abraham, A., Konar, A.: Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm. Pattern Recogn. Lett. 29(5), 688–699 (2008)

    Article  Google Scholar 

  15. Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chržtien, L.: The dynamics of collective sorting: robot-like ants and ant-like robots. In: Meyer, J.A., Wilson, S. (eds.) From Animals to Animats: Proceedings of the 1st International Conference on Simulation of Adaptive Behavior, pp. 356–365. Cambridge, MIT Press (2008)

    Google Scholar 

  16. Dorigo, M., Maniezzo, V., Colorni, A.: Positive feedback as a search strategy. Technical Report 91016 Dipartimento di Elettronica e Informatica, Politecnico di Milano, Italy (1991)

    Google Scholar 

  17. Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B Cybern. 26(1), 29–41 (1996)

    Article  Google Scholar 

  18. Elkamel, A., Gzara, M., Ben-Abdallah, H.: A bio-inspired hierarchical clustering algorithm with backtracking strategy. Appl. Intell. 42, 174–194 (2015)

    Article  Google Scholar 

  19. Ester, M., Kriegel, K.P., Sander J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)

    Google Scholar 

  20. Fathian, M., Amiri, B., Maroosi, A.: Application of honey-bee mating optimization algorithm on clustering. Appl. Math. Comput. 190(2), 1502–1513 (2007)

    MathSciNet  MATH  Google Scholar 

  21. Firouzi, B.B., Sadeghi, M.S., Niknam, T.: A new hybrid algorithm based on PSO, SA and k-means for cluster analysis. Int. J. Innovative Comput. Inf. Control 6(7), 3177–3192 (2010)

    Google Scholar 

  22. Ghosh, A., Halder, A., Kothari, M., Ghosh, S.: Aggregation pheromone density based data clustering. Inf. Sci. 178(13), 2816–2831 (2008)

    Article  Google Scholar 

  23. Gower, J.: Coefficients of association and similarity, based on binary (presence-absence) data: an evaluation. Biometrics 27, 857–871 (1971)

    Article  Google Scholar 

  24. Grosan, C., Abraham, A., Chis, M.: Swarm Intelligence in Data Mining. In: Abraham, A., Grosan, C., Ramos, V. (eds.) Swarm Intelligence in Data Mining, pp. 1–20. Springer, Berlin (2006)

    Chapter  Google Scholar 

  25. Guha, S., Rastogi, R., Shim, K.: CURE: An efficient clustering algorithm for large databases. In: Proceedings of the ACM-SIGMOD International Conference on Management of Data, pp. 73–84 (1998)

    Google Scholar 

  26. Gunes, O.G., Uyar, A.S.: Parallelization of an ant-based clustering approach. Kybernetes 39(4), 656–677 (2010)

    Article  Google Scholar 

  27. Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufman, Massachusetts (2011)

    MATH  Google Scholar 

  28. Handl, J., Meyer, B.: Ant-based and swarm-based clustering. Swarm Intell. 1, 95–113 (2007)

    Article  Google Scholar 

  29. Handl, J., Knowles, J., Dorigo, M.: Ant-based clustering and topographic mapping. Artif. Life 12, 35–61 (2006)

    Article  Google Scholar 

  30. Hruschka, E.R., Campello, R.J.G.B., de Castro, L.N.: Evolving clusters in gene-expression data. Inf. Sci. 176(13), 1898–1927 (2006)

    Article  MathSciNet  Google Scholar 

  31. Huang, C.L., Huang, W.C., Chang, H.Y., Yeh, Y.C., Tsai, C.Y.: Hybridization strategies for continuous ant colony optimization and particle swarm optimization applied to data clustering. Appl. Soft Comput. 13(9), 3864–3872 (2013)

    Article  Google Scholar 

  32. Ichino, M., Yaguchi, H.: Generalized Minkowski metrics for mixed feature-type data analysis. IEEE Trans. Syst. Man Cybern. 24(4), 698–708 (1994)

    Article  MathSciNet  Google Scholar 

  33. İnkaya, T., Kayalıgil, S., Özdemirel, N.E.: Ant colony optimization based clustering methodology. Appl. Soft Comput. 28, 301–311 (2015)

    Article  Google Scholar 

  34. Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31, 651–666 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  36. Jarboui, B., Cheikh, M., Siarry, P., Rebai, A.: Combinatorial particle swarm optimization for partitional clustering problem. Appl. Math. Comput. 192, 337–345 (2007)

    MathSciNet  MATH  Google Scholar 

  37. Jiang, B., Wang, N.: Cooperative bare-bone particle swarm optimization for data clustering. Soft Comput. 18, 1079–1091 (2014)

    Article  Google Scholar 

  38. Jiang, L., Ding, L., Peng, Y.: An efficient clustering approach using ant colony algorithm in multidimensional search space. In: Proceedings of the 8th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 1085–1089 (2011)

    Google Scholar 

  39. Kao, Y.T., Zahara, E., Kao, I.W.: A hybridized approach to data clustering. Expert Syst. Appl. 34(3), 1754–1762 (2008)

    Article  Google Scholar 

  40. Karaboga, D.: An idea based on honey bee swarm for numerical optimization, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)

    Google Scholar 

  41. Karaboga, D., Ozturk, C.: A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl. Soft Comput. 11(1), 652–657 (2011)

    Article  Google Scholar 

  42. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Network, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  43. Khereddine, B., Gzara, M.: FDClust: a new bio-inspired divisive clustering algorithm. Advances in Swarm Intelligence. Lecture Notes in Computer Science, vol. 6729, pp. 136–145. Springer, Berlin (2011)

    Google Scholar 

  44. Kountche, D.A., Monmarche, N., Slimane, M.: The Pachycondyla Apicalis ants search strategy for data clustering problems. Swarm and Evolutionary Computation. Lecture Notes in Computer Science, vol. 7269, pp. 3–11. Springer, Berlin (2012)

    Google Scholar 

  45. Kuo, R.J., Wang, M.J., Huang, T.W.: An application of particle swarm optimization algorithm to clustering analysis. Soft Comput. 15, 533–542 (2011)

    Article  Google Scholar 

  46. Lu, Y., Wang, S., Li, S., Zhou, C.: Particle swarm optimizer for variable weighting in clustering high-dimensional data. Mach. Learn. 82, 43–70 (2011)

    Article  MathSciNet  Google Scholar 

  47. Lumer, E.D., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: Cliff, D., Husbands, P., Meyer, J.A., Wilson, S.W. (eds.) Proceedings of the 3rd International Conference on Simulation of Adaptive Behavior: From Animals to Animats, pp. 501–508. MIT Press/Bradford Books, Cambridge (1994)

    Google Scholar 

  48. Martens, D., Baesens, B., Fawcett, T.: Editorial survey: swarm intelligence for data mining. Mach. Learn. 82, 1–42 (2011)

    Article  MathSciNet  Google Scholar 

  49. Martin, M., Chopard, B., Albquerque, P.: Formation of an ant cemetry: swarm intelligence or statistical accident? Futur. Gener. Comput. Syst. 18(7), 951–959 (2002)

    Article  MATH  Google Scholar 

  50. Nanda, S. J., Panda, G.: A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol. Comput. 16, 1–18 (2014)

    Article  Google Scholar 

  51. Niknam, T., Amiri, B.: An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Appl. Soft Comput. 10(1), 183–197 (2010)

    Google Scholar 

  52. Omran, M., Salman, A., Engelbrecht, A.P.: Image classification using particle swarm optimization. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning, pp. 18–22 (2002)

    Google Scholar 

  53. Omran, M., Engelbrecht, A.P., Salman, A.: Particle swarm optimization method for image clustering. Int. J. Pattern Recognit. Artif. Intell. 19(3), 297–321 (2005)

    Article  Google Scholar 

  54. Omran, M.G.H., Salman, A., Engelbrecht, A.P.: Dynamic clustering using particle swarm optimization with application in image segmentation. Pattern. Anal. Appl. 8, 332–344 (2006)

    Article  MathSciNet  Google Scholar 

  55. Paterlini, S., Krink, T.: Differential evolution and particle swarm optimisation in partitional clustering. Comput. Stat. Data Anal. 50, 1220–1247 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  56. Picarougne, F., Azzag, H., Venturini, G., Guinot, C.: A new approach of data clustering using a flock of agents. Evol. Comput. 15(3), 345–367 (2007)

    Article  Google Scholar 

  57. Rana, S., Jasola, S., Kumar, R.: A review on particle swarm optimization algorithms and their applications to data clustering. Artif. Intell. Rev. 35, 211–222 (2011)

    Article  Google Scholar 

  58. Runkler, T.A.: Ant colony optimization in clustering models. Int. J. Intell. Syst. 20, 1233–1251 (2005)

    Article  MATH  Google Scholar 

  59. Runkler, T.A.: Wasp swarm optimization of the c-means clustering model. Int. J. Intell. Syst. 23, 269–285 (2008)

    Article  MATH  Google Scholar 

  60. Senthilnath, J., Omkar, S.N., Mani, V.: Clustering using firefly algorithm: performance study. Swarm Evol. Comput. 1, 164–171 (2011)

    Article  Google Scholar 

  61. Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: An ant colony approach for clustering. Anal. Chim. Acta 509, 187–195 (2004)

    Article  Google Scholar 

  62. Sinha, A.N., Das, N., Sahoo, G.: Ant colony based hybrid optimization for data clustering. Kybernetes 36(2), 175–191 (2007)

    Article  MATH  Google Scholar 

  63. Song, W., Ma, W., Qiao, Y.: Particle swarm optimization algorithm with environmental factors for clustering analysis. Soft Comput. (2014). doi: 10.1007/s00500-014-1458-7

    Google Scholar 

  64. Tsai, C., Tsai, C., Wu, H., Yang, T.: ACODF: a novel data clustering approach for data mining in large databases. J. Syst. Softw. 73, 133–145 (2004)

    Article  Google Scholar 

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

    Google Scholar 

  66. Veenhuis, C., Köppen, M.: Data swarm clustering. In: Abraham, A., Grosan, C., Ramos, V. (eds.) Swarm Intelligence in Data Mining, pp. 221–241. Springer, Berlin (2006)

    Chapter  Google Scholar 

  67. Vizine, A.L., De Castro, L.N., Hruschka, E.R., Gudwin, R.R.: Towards improving clustering ants: an adaptive ant clustering algorithm. Informatica 29, 143–154 (2005)

    MATH  Google Scholar 

  68. Wan, M., Wang, C., Li, L., Yang, Y.: Chaotic ant swarm approach for data clustering. Appl. Soft Comput. 12(8), 2387–2393 (2012)

    Article  Google Scholar 

  69. Xiao, X., Dow, E.R., Eberhart, R., Miled, Z.B., Oppelt, R.J.: A hybrid self-organizing maps and particle swarm optimization approach. Concurrency Comput. Pract. Exp. 16, 895–915 (2004)

    Article  Google Scholar 

  70. Xu, R., Xu, J., Wunsch, D.C.: A comparison study of validity indices on swarm-intelligence-based clustering. IEEE Trans. Syst. Man Cybern. B Cybern. 12(4), 1243–1256 (2012)

    Google Scholar 

  71. Xu, X., Lu, L., He, P., Pan, Z., Chen, L.: Improving constrained clustering via swarm intelligence. Neurocomputing 116, 317–325 (2013)

    Article  Google Scholar 

  72. Yan, X., Zhu, Y., Zou, W., Wang, L.: A new approach for data clustering using hybrid artificial bee colony. Neurocomputing 97, 241–25 (2012)

    Article  Google Scholar 

  73. Yang, F., Sun, T., Zhang, C.: An efficient hybrid data clustering method based on k-harmonic means and particle swarm optimization. Expert Syst. Appl. 36(6), 9847–9852 (2009)

    Article  Google Scholar 

  74. Yang, Y., Kamel, M.S.: An aggregated clustering approach using multi-ant colonies algorithms. Pattern Recogn. 39(7), 1278–1289 (2006)

    Article  MATH  Google Scholar 

  75. Zhang, C., Ouyang, D., Ning, J.: An artificial bee colony approach for clustering. Expert Syst. Appl. 37(7), 4761–4767 (2010)

    Article  Google Scholar 

  76. Zhang, L., Cao, Q.: A novel ant-based clustering algorithm using the kernel method. Inf. Sci. 181(20), 4658–4672 (2011)

    Article  MathSciNet  Google Scholar 

  77. Zhang, L., Cao, Q., Lee, J.: A novel ant-based clustering algorithm using Renyi entropy. Appl. Soft Comput. 13(5), 2643–2657 (2013)

    Article  Google Scholar 

  78. Zou, W., Zhu, Y., Chen, H., Sui, X.: A clustering approach using cooperative artificial bee colony algorithm. Discret. Dyn. Nat. Soc. 459796, 1–16 (2010)

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Industrial Engineering Department, Uludağ University, Görükle, 16059, Bursa, Turkey

    Tülin İnkaya

  2. Industrial Engineering Department, Middle East Technical University, Çankaya, 06800, Ankara, Turkey

    Sinan Kayalıgil & Nur Evin Özdemirel

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  1. Tülin İnkaya
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  2. Sinan Kayalıgil
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Correspondence to Tülin İnkaya .

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Editors and Affiliations

  1. Computer Science, Louisiana State University in Shreveport, Shreveport, Louisiana, USA

    M. Emre Celebi

  2. North American University, Houston, Texas, USA

    Kemal Aydin

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İnkaya, T., Kayalıgil, S., Özdemirel, N.E. (2016). Swarm Intelligence-Based Clustering Algorithms: A Survey. In: Celebi, M., Aydin, K. (eds) Unsupervised Learning Algorithms. Springer, Cham. https://doi.org/10.1007/978-3-319-24211-8_12

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