Skip to main content
SpringerLink
Log in

Optimizing Clustering with Cuttlefish Algorithm

  • Conference paper
  • First Online:
Information Technology, Systems Research, and Computational Physics (ITSRCP 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((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.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight 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

References

  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 2012

    Google Scholar 

  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. Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pérez, J.M.: An extensive comparative study of cluster validity indices. Pattern Recogn. 46(1), 243–256 (2013)

    Article  Google Scholar 

  4. Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat.-Theory Methods 3(1), 1–27 (1974)

    Article  MathSciNet  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. Fränti, P., Virmajoki, O.: Iterative shrinking method for clustering problems. Pattern Recogn. 39(5), 761–775 (2006)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  10. Kowalski, P.A., Kulczycki, P.: Interval probabilistic neural network. Neural Comput. Appl. 28(4), 817–834 (2017)

    Article  Google Scholar 

  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. 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. 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. 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. 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. 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)

    Article  MathSciNet  Google Scholar 

  17. Lichman, M.: UCI Machine Learning Repository (2013)

    Google Scholar 

  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 2008

    Google Scholar 

  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 2016

    Google Scholar 

  20. Quinlan, J.S.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)

    Google Scholar 

  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. 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. Senthilnath, J., Omkar, S.N., Mani, V.: Clustering using firefly algorithm: performance study. Swarm Evol. Comput. 1(3), 164–171 (2011)

    Article  Google Scholar 

  24. Setiono, R., Leow, W.K.: Vehicle recognition using rule based methods. Turing Institute Research Memorandum TIRM-87-018, 121 (1987)

    Google Scholar 

  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. Welch, W.J.: Algorithmic complexity: three NP- hard problems in computational statistics. J. Stat. Comput. Simul. 15(1), 17–25 (1982)

    Article  MathSciNet  Google Scholar 

  27. Zhang, J.: Selecting typical instances in instance-based learning. In: Proceedings of the Ninth International Conference on Machine Learning, pp. 470–479 (1992)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059, Cracow, Poland

    Piotr A. Kowalski, Szymon Łukasik & Piotr Kulczycki

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Piotr A. Kowalski, Szymon Łukasik, Małgorzata Charytanowicz & Piotr Kulczycki

  3. Electrical Engineering and Computer Science Faculty, Lublin University of Technology, Nadbystrzycka 36A, 20-618, Lublin, Poland

    Małgorzata Charytanowicz

Authors
  1. Piotr A. Kowalski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Szymon Łukasik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Małgorzata Charytanowicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Piotr Kulczycki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Piotr A. Kowalski .

Editor information

Editors and Affiliations

  1. Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Krakow, Poland

    Piotr Kulczycki

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Faculty of Engineering Sciences, Széchenyi István University, Győr, Hungary

    László T. Kóczy

  4. Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovakia

    Radko Mesiar

  5. Department of Electronic Systems, Aalborg University, Aalborg, Denmark

    Rafal Wisniewski

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kowalski, P.A., Łukasik, S., Charytanowicz, M., Kulczycki, P. (2020). Optimizing Clustering with Cuttlefish Algorithm. In: Kulczycki, P., Kacprzyk, J., Kóczy, L., Mesiar, R., Wisniewski, R. (eds) Information Technology, Systems Research, and Computational Physics. ITSRCP 2018. Advances in Intelligent Systems and Computing, vol 945. Springer, Cham. https://doi.org/10.1007/978-3-030-18058-4_3

Download citation

Publish with us

Policies and ethics

Search

Navigation