Abstract
K-means is a popularly used clustering algorithm. Results of k-means clustering algorithm are sensitive to initial centroids chosen that give different clustering results for different runs. The algorithm converges to local optima based on the initial centroids chosen and does not guarantee reaching the global optima. This paper proposes an algorithm for choosing the initial centroids where each initial centroid is determined using the feature means and eliminating its nearest neighbors for choosing the next centroid.
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References
P.S. Bradley, U.M. Fayyad, Refining initial points for K-means clustering, in Proceedings of the 15th International Conference on Machine Learning (ICML98), ed. by J. Shavlik (Morgan Kaufmann, San Francisco, 1998), pp. 91–99
M. Meila, D. Heckerman, An experimental comparison of several clustering and initialization methods, in UAI’98 Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence
J.M. Penã, J.A. Lozano, P. Larrañaga, An empirical comparison of four initialization methods for the K-means algorithm (1999)
S.S. Khan, A. Ahmad, Cluster center initialization algorithm for K-means clustering. Pattern Recogn. Lett. 25, 1293–1302 (2004)
T. Gonzalez, Clustering to minimize the maximum inter cluster. Theor. Comput. Sci. 38(2–3), 293–306 (1985)
I. Kotsavounidis, C.-C.J. Kuo, Z. Zhang, A new initialization technique for generalized Llioyd iteration. IEEE Sig. Process. Lett. 1(10), 144–146 (1994)
A.R. Barakbah, Y. Kiyoki, A pillar algorithm for K-means optimization for initial centroid designation, in IEEE Symposium on Computational Intelligence and Data Mining (CIDM) (2009)
B.B. Bhusare, S.M. Bansode, Centroids initialization for K-means clustering using improved pillar algorithm. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 3(4), 1317–1322 (2014)
K. Krishna, M. Narasimha Murty, Genetic K-means algorithm. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 29(3), 433–439 (1999)
Y. Lu, S. Lu, F. Fotouhi, Y. Deng, S.J. Brown, FGKA: a fast-genetic K-means clustering algorithm, in Published 2004 in SAC (2004)
B.A. Shboul, S.-H. Myaeng, Initializing K-means using genetic algorithms. Int. J. Comput. Inf. Eng.
U. Maulik, S. Bandyopadhyay, Genetic algorithm-based clustering technique. J. Pattern Recogn. Soc. 33, 1455–1465 (2000)
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Lakshmi, M.A., Victor Daniel, G., Srinivasa Rao, D. (2019). Initial Centroids for K-Means Using Nearest Neighbors and Feature Means. In: Wang, J., Reddy, G., Prasad, V., Reddy, V. (eds) Soft Computing and Signal Processing . Advances in Intelligent Systems and Computing, vol 900. Springer, Singapore. https://doi.org/10.1007/978-981-13-3600-3_3
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DOI: https://doi.org/10.1007/978-981-13-3600-3_3
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