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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Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM comput. Surv. (CSUR). 31, 264–323 (1999)
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)
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
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)
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)
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)
Chakraborty, A., Kar, A.K.: Swarm intelligence: a review of algorithms. In: Nature-Inspired Computing and Optimization, pp. 475–494. Springer, Berlin (2017)
de Amorim, R.C., Makarenkov, V.: Applying subclustering and L p distance in Weighted K-Means with distributed centroids. Neurocomputing 173, 700–707 (2016)
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)
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)
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)
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)
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)
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)
Mahdavi, M., Abolhassani, H.: Harmony k-means algorithm for document clustering. Data Min. Knowl. Discov. 18(3), 370–391 (2009)
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)
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)
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)
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)
Ishioka, T.: Extended k-means with an efficient estimation of the number of clusters. Ouyou toukeigaku, 29(3), 141–149 (2000)
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)
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)
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)
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)
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)
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
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
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)
Hatamlou, A.: A Hybrid Bio-inspired Algorithm and its Application. Applied Intelligence, pp. 1–9. Springer, New York (2017)
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
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)
Yazdani, M., Jolai, F.: Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J. Comput. Des. Eng. 3(1), 24–36 (2016)
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
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
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)
Yang, X.S.: A new metaheuristic Bat inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), pp. 65–74. Springer, Berlin (2010)
Gandomi, A.H., Alavi, A.H.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012)
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
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.
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
Komarasamy, G., Wahi, A.: An optimized k-means clustering technique using bat algorithm. Eur. J. Sci. Res. 84(2), 263–273 (2012)
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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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