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A Hybrid Bio-Inspired Tabu Search Clustering Approach

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Hybrid Artificial Intelligent Systems (HAIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12886))

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Abstract

The purpose of a data clustering process is to group a set of objects into multiple classes so that the objects in each class – cluster are similar according to certain rules or criteria, where the definition of similarity can be problem dependent. This paper is focused on a new bio-inspired clustering approach based on a model for combining tabu search algorithm (TS) and firefly algorithm (FF). The proposed hybrid bio-inspired system is tested on two well-known Iris and Wine data sets. Finally, the experimental results are compared with the parallel tabu search clustering algorithm. The proposed bio-inspired TS-FF clustering system shows a significantly better accuracy value for Iris data set.

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References

  1. Kendall, M. G.: The basic problems in cluster analysis. In: Cacoullous, T., (ed.) Discriminamt Analysis and Applications, pp. 179–191. Academic Press, Inc., New York (1973). https://doi.org/10.1016/B978-0-12-154050-0.50016-2

  2. Simić, D., Svirčević, V., Sremac, S., Ilin, V., Simić, S.: An efficiency k-means data clustering in cotton textile imports. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds.) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. AISC, vol. 403, pp. 255–264. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-26227-7_24

    Chapter  Google Scholar 

  3. Simić, D., Jackowski, K., Jankowski, D., Simić, S.: Comparison of clustering methods in cotton textile industry. In: Jackowski, K., Burduk, R., Walkowiak, K., Woźniak, M., Yin, H. (eds.) IDEAL 2015. LNCS, vol. 9375, pp. 501–508. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24834-9_58

    Chapter  Google Scholar 

  4. Simić, D., Ilin, V., Tanackov, I., Svirčević, V., Simić, S.: A hybrid analytic hierarchy process for clustering and ranking best location for logistics distribution center. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds.) HAIS 2015. LNCS (LNAI), vol. 9121, pp. 477–488. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19644-2_40

    Chapter  Google Scholar 

  5. Simić, D., Ilin, V., Svirčević, V., Simić, S.: A hybrid clustering and ranking method for best positioned logistics distribution Centre in Balkan Peninsula. Logic J. IGPL 25(6), 991–1005 (2017). https://doi.org/10.1093/jigpal/jzx047

    Article  MathSciNet  Google Scholar 

  6. Krawczyk, B., Simić, D., Simić, S., Woźniak, M.: Automatic diagnosis of primary head- aches by machine learning methods. Open Med. 8(2), 157–165 (2013). https://doi.org/10.2478/s11536-012-0098-5

    Article  Google Scholar 

  7. Simić, S., Sakač, S., Banković, Z., Villar, J.R., Simić, S.D., Simić, D.: A hybrid bio-inspired clustering approach for diagnosing children with primary headache disorder. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds.) HAIS 2020. LNCS (LNAI), vol. 12344, pp. 739–750. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61705-9_62

    Chapter  Google Scholar 

  8. Simić, S., Villar, J.R., Calvo-Rolle, J.L., Sekulić, S.R., Simić, S.D., Simić, D.: An application of a hybrid intelligent system for diagnosing primary headaches. Int. J. Environ. Res. Public Health 18(4), 1890 (2021). https://doi.org/10.3390/ijerph18041890

    Article  Google Scholar 

  9. Herrero, A., Jiménez, A., Alcalde, R.: Advanced feature selection to study the internationalization strategy of enterprises. Peer J. Comput. Sci. 7, e403 (2021). https://doi.org/10.7717/peerj-cs.403

    Article  Google Scholar 

  10. Ližbetinová, L., Štarchon, P., Lorincová, S., Weberová, D., Pruša, P.: Application of cluster analysis in marketing communications in small and medium-sized enterprises: an empirical study in the Slovak Republic. Sustainability 11, 2302 (2019). https://doi.org/10.3390/su11082302

    Article  Google Scholar 

  11. MacQueen, J.B: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. Berkeley, vol. 5.1, 281–297. University of California Press (1967)

    Google Scholar 

  12. Cao, B., Glover, F., Rego, C.: A Tabu search algorithm for cohesive clustering problems. J. Heuristics 21(4), 457–477 (2015). https://doi.org/10.1007/s10732-015-9285-2

    Article  Google Scholar 

  13. UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/datasets.html. Accessed 28 Feb 2018

  14. Hartigan, J.: Clustering Algorithms. John Wiley & Sons, New York (1975)

    MATH  Google Scholar 

  15. Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice-Hall, NJ (1988)

    MATH  Google Scholar 

  16. Woźniak, M.: Hybrid Classifiers: Methods of Data, Knowledge, and Classifier Combination. Springer, Berlin (2016)

    Google Scholar 

  17. Hennig, C., Meila, M., Murtagh, F., Rocci, R.: Handbook of Cluster Analysis. CRC Press, Chapman & Hall book, Boca Raton (2016)

    MATH  Google Scholar 

  18. Wierzchoń, S.T., Kłopotek, M.A.: Modern Algorithms of Cluster Analysis. Springer International Publishing AG, Cham, Switzerland (2018)

    Book  Google Scholar 

  19. Aggarwal, C.C., Reddy, C.K.: Data Clustering: Algorithms and Applications. CRC Press Inc., Taylor & Francis Group, LLC, Boca Raton (2013)

    Book  Google Scholar 

  20. Ketchen, D.J., Jr., Shook, C.L.: The application of cluster analysis in strategic management research: an analysis and critique. Strateg. Manage. J. 17(6), 441–458 (1996). https://doi.org/10.1002/(SICI)1097-0266(199606)

    Article  Google Scholar 

  21. Al-Sultan, K.S.: A Tabu search approach to the clustering problem. Pattern Recogn. 28(9), 1443–1451 (1995). https://doi.org/10.1016/0031-3203(95)00022-R

    Article  Google Scholar 

  22. Chu, S.-C., Roddick, J.F.: A clustering algorithm using the Tabu search approach with simulated annealing. In: Brebbia, C.A., Ebecken N.F.F. (Eds) Data Mining II. WIT Press (2000). ISBN 1–85312–821-X

    Google Scholar 

  23. Liu, Y., Liu, Y., Wang, L., Chen, K.: A hybrid Tabu search based clustering algorithm. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3682, pp. 186–192. Springer, Heidelberg (2005). https://doi.org/10.1007/11552451_25

    Chapter  Google Scholar 

  24. Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, UK (2008)

    Google Scholar 

  25. Glover, F.: Pseudo-centroid clustering. Soft. Comput. 21(22), 6571–6592 (2016). https://doi.org/10.1007/s00500-016-2369-6

    Article  Google Scholar 

  26. Lu, Y., Cao, B., Rego, C., Glover, F.: A Tabu search based clustering algorithm and its parallel implementation on Spark. Appl. Soft Comput. 63, 97–109 (2018). https://doi.org/10.1016/j.asoc.2017.11.038

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000, Novi Sad, Serbia

    Dragan Simić & Svetislav D. Simić

  2. Frontiers Media SA, Paseo de Castellana 77, Madrid, Spain

    Zorana Banković

  3. University of Oviedo, Campus de Llamaquique, 33005, Oviedo, Spain

    José R. Villar

  4. Department of Industrial Engineering, University of A Coruña, 15405, Ferrol-A Coruña, Spain

    José Luis Calvo-Rolle

  5. Faculty of Medicine, University of Novi Sad, 21000, Novi Sad, Serbia

    Svetlana Simić

Authors
  1. Dragan Simić
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  2. Zorana Banković
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  3. José R. Villar
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  4. José Luis Calvo-Rolle
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  5. Svetislav D. Simić
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  6. Svetlana Simić
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Corresponding author

Correspondence to Dragan Simić .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Hugo Sanjurjo González

  2. University of Deusto, Bilbao, Spain

    Iker Pastor López

  3. University of Deusto, Bilbao, Spain

    Pablo García Bringas

  4. University of A Coruña, A Coruña, Spain

    Héctor Quintián

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Simić, D., Banković, Z., Villar, J.R., Calvo-Rolle, J.L., Simić, S.D., Simić, S. (2021). A Hybrid Bio-Inspired Tabu Search Clustering Approach. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2021. Lecture Notes in Computer Science(), vol 12886. Springer, Cham. https://doi.org/10.1007/978-3-030-86271-8_37

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  • DOI: https://doi.org/10.1007/978-3-030-86271-8_37

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  • Online ISBN: 978-3-030-86271-8

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