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Hybrid metaheuristic algorithm for improving the efficiency of data clustering

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Abstract

Clustering is a technique which is used to group the data into different subgroups or subsets to retrieve meaningful information from the available huge dataset. The trending swarm based intelligent system replaces the conventional clustering algorithm with the objective of increased performance. Ant lion optimization (ALO) technique is the swarm based intelligence that exhibits the hunting mechanism of the ant lions in the natural environment. Ant colony optimization (ACO) algorithm is a swarm based intelligence technique which inherits the behaviour of natural ant. In this paper new hybrid ACO–ALO algorithm was proposed to solve the data clustering problem. Additionally Cauchy’s mutation operator is added with this proposed algorithm to avoid the local minima trapping problem. The main objective is to reduce the intra cluster distance in clustering problem. From the experimental analysis, it evidences the proposed ACO–ALO algorithm outperforms the traditional algorithms of data clustering.

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References

  1. Karaboga, D., Ozturk, C.: A novel clustering approach: artificial bee colony (ABC) algorithm. Appl. Soft Comput. 11, 652–657 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Huang, C.-L., Yeh, W.-C.: A new K-harmonic means based simplified swarm optimization for data mining. In: IEEE International Conference on Computer Science and Service System, pp. 1–10 (2014)

  4. Naeini, A.A., Homayouni, S.: Improving the dynamic clustering of hyperspectral data based on the integration of swarm optimization and decision analysis. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 7(6), 2161–2173 (2014)

    Article  Google Scholar 

  5. Tran, D.C., Wu, Z.: Data clustering based on particle swarm optimization with neighborhood search and cauchy mutation. In: International Conference on Neural Information Processing, pp. 151–159 (2014)

  6. Al-Baity, H., Meshoul, S., Kaban, A., AlSafadi, L.: Quantum behaved particle swarm optimization for data clustering with multiple objectives. In: 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR) (2014)

  7. Medeiros, I.G., Xavier-Junior, J.C., Canuto, A.M.P.: Applying the coral reefs optimization algorithm to clustering problems. In: International Joint Conference on Neural Networks, vol. 1 (2015)

  8. Kumar, Y., Sahoo, G.: An improved cat swarm optimization algorithm based on opposition-based learning and cauchy operator for clustering. J. Inf. Process. Syst. 13(4), 1000–1013 (2017)

    Google Scholar 

  9. Gao, W.: Improved ant colony clustering algorithm and its performance study. Comput. Intell. Neurosci. 2016, 19 (2016)

    Google Scholar 

  10. Hatamlou, A., Salwani, A., Nezamabadi-pour, H.: A combined approach for clustering based on K-means and gravitational search algorithms. Swarm Evol. Comput. 6, 47–52 (2011)

    Article  Google Scholar 

  11. Saida, I.B., Nadjet, K., Omar, B.: A new algorithm for data clustering based on cuckoo search optimization. In: Pan, J.S., Krömer, P., Snášel, V. (eds.) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol. 238, pp. 55–64. Springer, Cham (2014)

    Google Scholar 

  12. Liu, C., Wang, C., Hu, J., Ye, Z.: Improved K-means algorithm based on hybrid rice optimization algorithm. In: The 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (2017)

  13. Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)

    Article  Google Scholar 

  14. Burke, K., Gustafson, S., Kendall, G.: Diversity in genetic programming: an analysis of measures and correlation with fitness. IEEE Trans. Evol. Comput. 8(1), 47–62 (2004)

    Article  Google Scholar 

  15. Deneubourg, J.L., Aron, S., Goss, S., Pasteels, J.M.: The self-organizing exploratory pattern of the argentine ant. J. Insect Behav. 3, 159–168 (1990)

    Article  Google Scholar 

  16. Baum, E.B.: Iterated descent: a better algorithm for local search in combinatorial optimization problems. Technical report, Caltech, Pasadena, CA (1986)

    Google Scholar 

  17. Chiarandini, M., Stützle, T.: An application of iterated local search to graph coloring problem. In: Proceedings of the Computational Symposium on Graph Coloring and its Generalizations, pp. 112–125 (2002)

  18. Lourenço, H., Martin, O., Stützle, T.: Iterated local search. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics, pp. 321–353. Kluwer, Dordrecht (2003)

    Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, SNS College of Technology, Coimbatore, 641035, Tamilnadu, India

    C. Mageshkumar

  2. Department of Computer Science & Engineering, SNS College of Technology, Coimbatore, 641035, Tamilnadu, India

    S. Karthik & V. P. Arunachalam

Authors
  1. C. Mageshkumar
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  2. S. Karthik
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  3. V. P. Arunachalam
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Correspondence to C. Mageshkumar.

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Mageshkumar, C., Karthik, S. & Arunachalam, V.P. Hybrid metaheuristic algorithm for improving the efficiency of data clustering. Cluster Comput 22 (Suppl 1), 435–442 (2019). https://doi.org/10.1007/s10586-018-2242-8

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  • DOI: https://doi.org/10.1007/s10586-018-2242-8

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