Abstract
K-Means is one of the unsupervised learning and partitioning clustering algorithms. It is very popular and widely used for its simplicity and fastness. The main drawback of this algorithm is that user should specify the number of cluster in advance. As an iterative clustering strategy, K-Means algorithm is very sensitive to the initial starting conditions. In this paper, we propose a clustering technique called MaxD K-Means clustering algorithm. MaxD K-Means algorithm auto generates initial k (the desired number of cluster) without asking for input from the user. MaxD K-means also used a novel strategy of setting the initial centroids. The experiment of the Max-D means has been conducted using synthetic data, which is taken from the Llyod’s K-Means experiments. The results from the new algorithm show that the number of iteration improves tremendously, and the number of iterations is reduced by confirming an improvement rate is up to 78%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zhou, H., Liu, Y.: Accurate integration of multi-viewrange images using k-means clustering. Pattern Recognition 41, 152–175 (2008)
Bandyopadhyay, S., Maulik, U.: An evolutionary technique based on K-Means algorithm for optimal clustering. Information Sciences 146, 221–237 (2002)
Herawan, T., Yanto, I.T.R., Deris, M.M.: Rough Set Approach for Categorical Data Clustering. In: Ślęzak, D., Kim, T.-h., Zhang, Y., Ma, J., Chung, K.-i. (eds.) DTA 2009. CCIS, vol. 64, pp. 179–186. Springer, Heidelberg (2009)
Yanto, I.T.R., Herawan, T., Deris, M.M.: Data clustering using Variable Precision Rough Set. Intelligent Data Analysis 15(4), 465–482 (2011)
Yanto, I.T.R., Vitasari, P., Herawan, T., Deris, M.M.: Applying Variable Precision Rough Set Model for Clustering Student Suffering Study’s Anxiety. Expert System with Applications 39(1), 452–459 (2012)
Herawan, T., Yanto, I.T.R., Deris, M.M.: ROSMAN: ROugh Set approach for clustering supplier chain MANagement. International Journal of Biomedical and Human Sciences 16(2), 105–114 (2010)
Herawan, T., Deris, M.M., Abawajy, J.H.: A rough set approach for selecting clustering attribute. Knowledge Based Systems 23(3), 220–231 (2010)
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. John Wiley and Sons, New York (2001)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Spc. 39, 1–38 (1977)
McLachlan, G.L., Basford, K.E.: Mixture Models: Inference and Application to clustering. Marcel Dekker (1987)
Jiambo, S., Jitendra, M.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Machine Intell. 22, 288–905 (2000)
Stella, Y., Jianbo, S.: Multiclass spectral clustering. In: Proc. Internat. Conf. on Computer Vision, pp. 313–319 (2003)
Murino, L., Angelini, C., Feis, I.D., Raiconi, G., Tagliaferri, R.: Beyond classical consensus clustering: The least squares approach to multiple solutions. Pattern Recognition Letters 32, 1604–1612 (2011)
Dunham, M.: Data Mining: Introductory and Advance Topics. N.J. Prentice Hall (2003)
Chiang, M., Tsai, C., Yang, C.: A time-efficient pattern reduction algorithm for k-means clustering. Information Sciences 181, 716–731 (2011)
Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Transaction on Neural Netowrks 16(3), 645–678 (2005)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3) (1999)
Kanungo, T., Mount, D., Netanyahu, N.S., Piatko, C., Silverman, R., Wu, A.: An efficient K-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)
Likas, A., Vlassis, N., Verbeek, J.J.: The global K-means clustering algorithm. Pattern Recognition 36, 452–461 (2003)
Charalampidis, D.: A modified K-means algorithm for circular invariant clustering. IEEE Trans. Pattern Anal. Mach. Intell. 27(12), 1856–1865 (2005)
Selim, S.Z., Ismail, M.A.: K-means type algorithms: a generalized convergence theorem and characterization of local optimality. IEEE Trans. Pattern Anal. Mach. Intell. 6, 81–87 (1984)
Spath, H.: Cluster Analysis Algorithms. Ellis Horwood, Chichester (1989)
Chang, D., Xian, D., Chang, W.: A genetic algorithm with gene rearrangement for K-means clustering. Pattern Recognition 42, 1210–1222 (2009)
Otsubo, M., Sato, K., Yamaji, A.: Computerized identification of stress tensors determined from heterogeneous fault-slip data by combining the multiple inverse method and k-means clustering. Journal of Structural Geology 28, 991–997 (2006)
Kalyani, S., Swarup, K.S.: Particle swarm optimization based K-means clustering approach for security assessment in power systems. Expert Systems with Applications 38, 10839–10846 (2011)
Bagirov, A.M., Ugon, J., Webb, D.: Fast modified global k-means algorithm for incremental cluster construction. Pattern Recognition 44, 866–876 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mohd, W.M.W., Beg, A.H., Herawan, T., Rabbi, K.F. (2012). MaxD K-Means: A Clustering Algorithm for Auto-generation of Centroids and Distance of Data Points in Clusters. In: Li, Z., Li, X., Liu, Y., Cai, Z. (eds) Computational Intelligence and Intelligent Systems. ISICA 2012. Communications in Computer and Information Science, vol 316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34289-9_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-34289-9_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34288-2
Online ISBN: 978-3-642-34289-9
eBook Packages: Computer ScienceComputer Science (R0)