Cuckoo, Bat and Krill Herd based k-means++ clustering algorithms (original) (raw)

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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