Abstract
k-means is one of the most widely used partition based clustering algorithm. But the initial centroids generated randomly by the k-means algorithm cause the algorithm to converge at the local optimum. So to make k-means algorithm globally optimum, the initial centroids must be selected carefully rather than randomly. Though many researchers have already been carried out for the enhancement of k-means algorithm, they have their own limitations. In this paper a new method to formulate the initial centroids is proposed which results in better clusters equally for uniform and non-uniform data sets.
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References
S.A. Rauf, S. Mahfooz, S. Khusro, H. Javed, Enhanced k-mean clustering algorithm to reduce number of iterations and time complexity. Middle-East J. Sci. Res. 12(7), 959–963 (2012)
C.S. Li, Cluster center initialization method for k-means algorithm over data sets with two clusters, in Proceedings of International Conference on Advances in Engineering, pp. 324–328, 2011
C. Zhang, Z. Fang, An improved k-means clustering algorithm. J. Inf. Comput. Sci. 10(1), 193–199 (2013)
F. Yuan, A new algorithm to get the initial centroids, in Proceedings of the Third IEEE International Conference on Machine Learning and Cybernetics, Shanghai, pp. 1191–1193, 2004
K.A.A. Nazeer, M.P. Sebastian, Improving the accuracy and efficiency of the k-means clustering algorithm, in Proceedings of the World Congress on Engineering, vol. 1, ISBN: 978-988-17012-5-1, 2009
K. Arai, A.R. Barakbah, Hierarchical k-means: an algorithm for centroids initialization for k-means. Rep. Fac. Sci. Eng. Saga Univ. 36(1), 25–31 (2007)
Md.E. Agha, W.M. Ashour, Efficient and fast initialization algorithm for k-means clustering. Int. J. Intell. Syst. Appl. 1, 21–31 (2012)
M.E. Celebi, H.A. Kingravi, P.A. Vela, A comparative study of efficient initialization methods for the k-means clustering algorithm. J. Exp. Syst. Appl. 40(1), 200–210 (2013)
M. Yedla, S. Rao Pathakota, T.M. Srinivasa, Enhancing k-means clustering algorithm with improved initial center. IJCSIT 1(2), 121–125 (2010)
R.T. Aldahdooh, W. Ashour, DIMK means: distance based initialization method for k-means clustering algorithm. Int. J. Intell. Syst. Appl. 5(2), 41–51 (2013)
S.S. Khan, A. Ahmad, Cluster center initialization algorithm for k-means clustering. Elesvier J. Pattern Recogn. Lett. 25, 1293–1302 (2004)
X. Zhang, Q. Shen, H. Gao, Z. Zhao, S. Ci, A density-based method for initializing the k-means clustering algorithm, in Proceedings of International Conference on Network and Computational Intelligence (ICNCI 2012), IPCSIT, vol. 46, pp. 46–53, 2012
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Goyal, M., Kumar, S. Improving the Initial Centroids of k-means Clustering Algorithm to Generalize its Applicability. J. Inst. Eng. India Ser. B 95, 345–350 (2014). https://doi.org/10.1007/s40031-014-0106-z
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DOI: https://doi.org/10.1007/s40031-014-0106-z