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Cuckoo, Bat and Krill Herd based k-means++ clustering algorithms

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Abstract

Traditional k-means clustering algorithm is sensitive to the choice of initial cluster centers and leads to local optimal results. k-means++ is a hybrid k-means clustering algorithm which specifies the procedure to initialize the cluster centers before proceeding with the standard k-means algorithm. Inspired by nature, some contemporary optimization techniques such as Cuckoo, Bat and Krill Herd algorithms etc., are used for optimization as they mimic the swarming behaviour and allows to cooperatively move towards an optimal objective within a reasonable time. In this paper, these nature-inspired techniques are used for optimizing k-means++ clustering algorithm to enhance clustering quality and generate new hybrids of unprecedented performance. The results of the evaluation experiments on the integration of nature-inspired optimization methods with k-means++ algorithm are reported.

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References

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

    Article  Google Scholar 

  2. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)

  3. Zang, H., Zhang, S., Hapeshi, K.: A review of nature-inspired algorithms. J. Bionic Eng. 7, 232–237 (2010). https://doi.org/10.1016/S1672-6529(09)60240-7

    Article  Google Scholar 

  4. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS’95, pp. 39–43 (1995)

  5. Maniezzo, A.C.: Distributed optimization by ant colonies. In: Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, Mit Press, pp. 134–152 (1992)

  6. Yang, X.S., Deb, S.: Eagle strategy using Lévy walk and firefly algorithms for stochastic optimization. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 101–111. Springer, Berlin (2010)

    Chapter  Google Scholar 

  7. Chakraborty, A., Kar, A.K.: Swarm intelligence: a review of algorithms. In: Nature-Inspired Computing and Optimization, pp. 475–494. Springer, Berlin (2017)

    Chapter  Google Scholar 

  8. de Amorim, R.C., Makarenkov, V.: Applying subclustering and L p distance in Weighted K-Means with distributed centroids. Neurocomputing 173, 700–707 (2016)

    Article  Google Scholar 

  9. Jothi, R., Mohanty, S.K., Ojha, A.: On careful selection of initial centers for k-means algorithm. In: Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics, pp. 435–445. Springer, New Delhi (2016)

    Google Scholar 

  10. Sawaqed, L., AlShabi, M., Alshaer, S., Salameh, I.: An improved k-means clustering algorithm for two half-moon classification. In: IEEE 10th International Symposium on Mechatronics and its Applications (ISMA), pp. 1–4 (2015)

  11. Ayech, M.W., Ziou, D.: Segmentation of Terahertz imaging using k-means clustering based on ranked set sampling. Expert. Syst. Appl. 42(6), 2959–2974 (2015)

    Article  Google Scholar 

  12. Dhillon, I.S., Guan Y., Kulis, B.: Kernel k-means: spectral clustering and normalized cuts. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 551-556. ACM (2004)

  13. Singh, R.V., Bhatia, M.P.S.: Data clustering with modified k-means algorithm. In: 2011 International Conference on Recent Trends in Information Technology (ICRTIT), pp. 717–721. IEEE (2011)

  14. Bhavani, R., Sadasivam, G.S., Kumaran, R.: A novel parallel hybrid k-means-DE-ACO clustering approach for genomic clustering using MapReduce. In: World Congress on Information and Communication Technologies (WICT), pp. 132–137 (2011)

  15. Mahdavi, M., Abolhassani, H.: Harmony k-means algorithm for document clustering. Data Min. Knowl. Discov. 18(3), 370–391 (2009)

    Article  MathSciNet  Google Scholar 

  16. Li, M.J., Ng, M.K., Cheung, Y.M. and Huang, J.Z.: Agglomerative fuzzy k-means clustering algorithm with selection of number of clusters. IEEE Trans. Knowl. Data Eng. 20(11), 1519–1534 (2008)

    Article  Google Scholar 

  17. Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACMSIAM Symposium on Discrete Algorithms, Society for Industrial and Applied Mathematics, pp. 1027–1035 (2007)

  18. Fahim, A.M., Salem, A.M., Torkey, F.A., Ramadan, M.A.: An efficient enhanced k-means clustering algorithm. J. Zhejiang Univ. Sci. 7(10), 1626–1633 (2006)

    Article  Google Scholar 

  19. Zhang, Q., Couloigner, I.: A new and efficient k-medoid algorithm for spatial clustering. In: International Conference on Computational Science and Its Applications, pp. 181–189. Springer, Berlin, Heidelberg, (2005)

    Chapter  Google Scholar 

  20. Ishioka, T.: Extended k-means with an efficient estimation of the number of clusters. Ouyou toukeigaku, 29(3), 141–149 (2000)

    Article  Google Scholar 

  21. Su, M.C., Chou, C.H.: A modified version of the k-means algorithm with a distance based on cluster symmetry. IEEE Trans. Pattern. Anal. Mach. Intell. 23(6), 674–680 (2001)

    Article  Google Scholar 

  22. Tang, R., Fong, S., Yang, X.S., Deb, S.: Integrating nature-inspired optimization algorithms to k-means clustering. In: Seventh International Conference on Digital Information Management (ICDIM), 2012, pp. 116–123. IEEE (2012)

  23. Nayak, J., Kanungo, D.P., Naik, B., Behera, H.S.: Evolutionary improved swarm-based hybrid k-means algorithm for cluster analysis. In: Proceedings of the Second International Conference on Computer and Communication Technologies, pp. 343–352. Springer, New Delhi (2016)

    Google Scholar 

  24. Hatamlou, A., Abdullah, S., Nezamabadi-pour, H.: A combined approach for clustering based on k-means and gravitational search algorithms. Swarm Evolut. Comput. 6, 47–52 (2012)

    Article  Google Scholar 

  25. Ahmed, H., Shedeed, H.A., Hamad, S., Tolba, M.F.: On combining nature-inspired algorithms for data clustering. In: Handbook of Research on Machine Learning Innovations and Trends, pp. 826–855. IGI Global, Hershey (2017)

  26. Yan, X., Liu, H., Zhu, Z., et al.: Hybrid genetic algorithm for engineering design problems. Cluster Comput. 20, 263 (2017). https://doi.org/10.1007/s10586-016-0680-8

    Article  Google Scholar 

  27. Meng, X., Dong, L., Li, Y., et al.: A genetic algorithm using K-path initialization for community detection in complex networks. Cluster Comput. 20, 311 (2017). https://doi.org/10.1007/s10586-016-0698-y

    Article  Google Scholar 

  28. Tran, D.C., Wu, Z.: A new approach of dynamic clustering based on particle swarm optimization and application in image segmentation. Comput. Inf. 36(3). http://www.cai.sk/ojs/index.php/cai/article/view/2017_3_637 (2017)

    Article  MathSciNet  Google Scholar 

  29. Hatamlou, A.: A Hybrid Bio-inspired Algorithm and its Application. Applied Intelligence, pp. 1–9. Springer, New York (2017)

    Google Scholar 

  30. Pei, J., Zhao, L., Dong, X., et al.: Effective algorithm for determining the number of clusters and its application in image segmentation. Cluster Comput. 20, 2845 (2017). https://doi.org/10.1007/s10586-017-1083-1

    Article  Google Scholar 

  31. Mohammed, A.J., Yusof, Y., Husni, H.: Gf-Clust: a nature-inspired algorithm for automatic text clustering. J. Inf. Commun. Technol. 15(1), 57–81 (2016)

    Google Scholar 

  32. Yazdani, M., Jolai, F.: Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J. Comput. Des. Eng. 3(1), 24–36 (2016)

    Google Scholar 

  33. Wen, F., Wang, X., Zhang, G.: Evolutionary-based automatic clustering method for optimizing multilevel network. Cluster Comput. 20, 3161 (2017). https://doi.org/10.1007/s10586-017-1030-1

    Article  Google Scholar 

  34. Siddique, N., Adeli, H.: Nature inspired computing: an overview and some future directions. Cogn. Comput. 7(6), 706–714 (2015). https://doi.org/10.1007/s12559-015-9370-8

    Article  Google Scholar 

  35. Yang, X.S., Deb, S.: Cuckoo Search via Lévy flights. In: Proceedings of the IEEE World Congress on Nature & Biologically Inspired Computing, NaBIC, pp. 210–214 (2009)

  36. Yang, X.S.: A new metaheuristic Bat inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), pp. 65–74. Springer, Berlin (2010)

    Chapter  Google Scholar 

  37. Gandomi, A.H., Alavi, A.H.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012)

    Article  MathSciNet  Google Scholar 

  38. Fong, Simon, Deb, Suash, Yang, Xin-She, Zhuang, Yan: Towards enhancement of performance of k-means clustering using nature-inspired optimization algorithms. Sci. World J (2014). https://doi.org/10.1155/2014/564829

    Article  Google Scholar 

  39. Saida, I.B., Nadjet, K., Omar, B.: A new algorithm for data clustering based on cuckoo search optimization. In: Genetic and Evolutionary Computing, pp. 55-64. Springer, Cham, 2014.

  40. Fister, I., Fong, S., Brest, J., Fister, I.: A novel hybrid self-adaptive Bat algorithm. Sci. World J. (2014). https://doi.org/10.1155/2014/709738

    Article  Google Scholar 

  41. Komarasamy, G., Wahi, A.: An optimized k-means clustering technique using bat algorithm. Eur. J. Sci. Res. 84(2), 263–273 (2012)

    Google Scholar 

  42. Nikbakht, H., Mirvaziri, H.: A new clustering approach based on k-means and Krill Herd algorithm. In: IEEE 23rd Iranian Conference on Electrical Engineering (ICEE), pp. 662–667 (2015)

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Authors and Affiliations

  1. Department of CSE, Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, India

    Shruti Aggarwal

  2. Department of CSE, National Institute of Technology, Jalandhar, Punjab, India

    Paramvir Singh

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  1. Shruti Aggarwal
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  2. Paramvir Singh
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Correspondence to Paramvir Singh.

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Aggarwal, S., Singh, P. Cuckoo, Bat and Krill Herd based k-means++ clustering algorithms. Cluster Comput 22 (Suppl 6), 14169–14180 (2019). https://doi.org/10.1007/s10586-018-2262-4

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  • DOI: https://doi.org/10.1007/s10586-018-2262-4

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