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Optimizing Clustering with Cuttlefish Algorithm

  • Piotr A. KowalskiEmail author
  • Szymon Łukasik
  • Małgorzata Charytanowicz
  • Piotr Kulczycki
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 945)

Abstract

The Cuttlefish Algorithm, a modern metaheuristic procedure, is a very recent solution to a broad-range of optimization tasks. The aim of the article is to outline the Cuttlefish Algorithm and to demonstrate its usability in data mining problems. In this paper, we apply this metaheuristic procedure for a clustering problem, with the Calinski-Harabasz index used as a measure of solution quality. To examine the algorithm performance, selected datasets from the UCI Machine Learning Repository were used. Furthermore, the well-known and commonly utilized k-means procedure was applied to the same data sets - to obtain a broader, independent comparison. The quality of generated results were assessed via the use of the Rand Index.

Keywords

Clustering Cuttlefish Algorithm Biologically inspired algorithm Optimization Metaheuristic 

References

  1. 1.
    Achtert, E., Goldhofer, S., Kriegel, H.P., Schubert, E., Zimek, A.: Evaluation of clusterings – metrics and visual support. In: 2012 IEEE 28th International Conference on Data Engineering, pp. 1285–1288, April 2012Google Scholar
  2. 2.
    Aeberhard, S., Coomans, D., De Vel, O.: Comparison of classifiers in high dimensional settings. Department Mathematics and Statistics, James Cook University of North Queensland, Australia, Technical report 92(02) (1992)Google Scholar
  3. 3.
    Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pérez, J.M.: An extensive comparative study of cluster validity indices. Pattern Recogn. 46(1), 243–256 (2013)CrossRefGoogle Scholar
  4. 4.
    Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat.-Theory Methods 3(1), 1–27 (1974)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Charytanowicz, M., Niewczas, J., Kulczycki, P., Kowalski, P.A., Łukasik, S., Żak, S.: Complete gradient clustering algorithm for features analysis of x-ray images. In: Pietka, E., Kawa, J. (eds.) Information Technologies in Biomedicine. Advances in Intelligent and Soft Computing, vol. 69, pp. 15–24. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Eesa, A.S., Brifcani, A.M.A., Orman, Z.: Cuttlefish algorithm-a novel bio-inspired optimization algorithm. Int. J. Sci. Eng. Res. 4(9), 1978–1986 (2013)Google Scholar
  7. 7.
    Eesa, A.S., Brifcani, A.M.A., Orman, Z.: A new tool for global optimization problems-cuttlefish algorithm. Int. J. Math. Comput. Nat. Phys. Eng. 8(9), 1208–1211 (2014)Google Scholar
  8. 8.
    Fränti, P., Virmajoki, O.: Iterative shrinking method for clustering problems. Pattern Recogn. 39(5), 761–775 (2006)CrossRefGoogle Scholar
  9. 9.
    Gorman, R.P., Sejnowski, T.J.: Analysis of hidden units in a layered network trained to classify sonar targets. Neural Netw. 1(1), 75–89 (1988)CrossRefGoogle Scholar
  10. 10.
    Kowalski, P.A., Kulczycki, P.: Interval probabilistic neural network. Neural Comput. Appl. 28(4), 817–834 (2017)CrossRefGoogle Scholar
  11. 11.
    Kowalski, P.A., Łukasik, S.: Experimental study of selected parameters of the krill herd algorithm. In: Intelligent Systems’2014, pp. 473–485. Springer Science Business Media (2015)Google Scholar
  12. 12.
    Kowalski, P.A., Łukasik, S., Charytanowicz, M., Kulczycki, P.: Clustering based on the krill herd algorithm with selected validity measures. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Federated Conference on Computer Science and Information Systems 2016 (FedCSIS 2016), Annals of Computer Science and Information Systems, vol. 8, pp. 79–87, Gdansk, Poland, September 2016. IEEE (2016)Google Scholar
  13. 13.
    Kowalski, P.A., Łukasik, S., Charytanowicz, M., Kulczycki, P.: Data clustering with grasshopper optimization algorithm. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Federated Conference on Computer Science and Information Systems 2017 (FedCSIS 2017), Annals of Computer Science and Information Systems, vol. 11, pp. 71–74, Prague, Czech Republic, September 2017. IEEE (2017)Google Scholar
  14. 14.
    Kowalski, P.A., Łukasik, S., Charytanowicz, M., Kulczycki, P.: Optimizing clustering with cuttlefish algorithm. In: Kulczycki, P., Kowalski, P.A., Łukasik, S. (eds.) Contemporary Computational Science, p. 74. AGH-UST Press, Cracow (2018)Google Scholar
  15. 15.
    Kowalski, P.A., Łukasik, S., Charytanowicz, M., Kulczycki, P.: Nature inspired clustering - use cases of krill herd algorithm and flower pollination algorithm. In: Kóczy, L.T., Medina, J., Ramírez-Poussa, E. (eds.) Interactions Between Computational Intelligence and Mathematics. Studies in Computational Intelligence, pp. 83–98. Springer International Publishing, Cham (2019)Google Scholar
  16. 16.
    Kulczycki, P., Charytanowicz, M., Kowalski, P.A., Łukasik, S.: The complete gradient clustering algorithm: properties in practical applications. J. Appl. Stati. 39(6), 1211–1224 (2012)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Lichman, M.: UCI Machine Learning Repository (2013)Google Scholar
  18. 18.
    Łukasik, S., Kowalski, P.A., Charytanowicz, M., Kulczycki, P.: Fuzzy models synthesis with kernel-density-based clustering algorithm. In: Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008, FSKD 2008, vol. 3, pp. 449–453, October 2008Google Scholar
  19. 19.
    Łukasik, S., Kowalski, P.A., Charytanowicz, M., Kulczycki, P.: Clustering using flower pollination algorithm and calinski-harabasz index. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 2724–2728, July 2016Google Scholar
  20. 20.
    Quinlan, J.S.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
  21. 21.
    Quinlan, J.S., Compton, P.J., Horn, K.A., Lazarus, L.: Inductive knowledge acquisition: a case study. In: Proceedings of the Second Australian Conference on Applications of Expert Systems, pp. 137–156. Addison-Wesley Longman Publishing Co., Inc. (1987)Google Scholar
  22. 22.
    Rokach, L., Maimon, O.: Clustering methods. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 321–352. Springer US (2005)Google Scholar
  23. 23.
    Senthilnath, J., Omkar, S.N., Mani, V.: Clustering using firefly algorithm: performance study. Swarm Evol. Comput. 1(3), 164–171 (2011)CrossRefGoogle Scholar
  24. 24.
    Setiono, R., Leow, W.K.: Vehicle recognition using rule based methods. Turing Institute Research Memorandum TIRM-87-018, 121 (1987)Google Scholar
  25. 25.
    Sigillito, V.G., Wing, S.P., Hutton, L.V., Baker, K.B.: Classification of radar returns from the ionosphere using neural networks. Johns Hopkins APL Tech. Dig. 10(3), 262–266 (1989)Google Scholar
  26. 26.
    Welch, W.J.: Algorithmic complexity: three NP- hard problems in computational statistics. J. Stat. Comput. Simul. 15(1), 17–25 (1982)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Zhang, J.: Selecting typical instances in instance-based learning. In: Proceedings of the Ninth International Conference on Machine Learning, pp. 470–479 (1992)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Piotr A. Kowalski
    • 1
    • 2
    Email author
  • Szymon Łukasik
    • 1
    • 2
  • Małgorzata Charytanowicz
    • 2
    • 3
  • Piotr Kulczycki
    • 1
    • 2
  1. 1.Faculty of Physics and Applied Computer ScienceAGH University of Science and TechnologyCracowPoland
  2. 2.Systems Research Institute, Polish Academy of SciencesWarsawPoland
  3. 3.Electrical Engineering and Computer Science FacultyLublin University of TechnologyLublinPoland

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