Abstract
This paper proposes a hierarchical two-division method that divides each mother subset of a data set at the same layer into two subsets along a dimension, and hierarchically divides the data set into a series of leaf subsets when the two-division process passes through each dimension of the data set. Then the initial cluster centers are picked out from the series of leaf subsets according to the rule that optimizes the dissimilarities among the initial cluster centers. Thus a new cluster center initialization method is developed. Experiments on real data sets show that the proposed cluster center initialization method is desirable.
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
MacQueen, J.B.: Some methods for classification and analysis of multivariate observation. In: Le Cam, L.M. (ed.) Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press (1967)
Khan, S.S., Ahmad, A.: Cluster center initialization algorithm for k-means clustering. Pattern Recognition Lett. 25(11), 1293–1302 (2004)
Redmond, S.J., Heneghan, C.: A method for initialising the K-means clustering algorithm using kd-trees. Patt. Recog. Letters 28(8), 965–973 (2007)
Erisoglu, M., Calis, N., Sakallioglu, S.: A new algorithm for initial cluster centers in k-means algorithm. Patt. Recog. Letters 32, 1701–1705 (2011)
Pena, J.M., Lozano, J.A.: An empirical comparison of four initialization methods for the k-means algorithm. Patt. Recog. Lett. 20(10), 1027–1040 (1999)
Steinley, D., Brusco, M.J.: Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques. J. of Classification 24(1), 99–121 (2007)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Chen, G.H. (2012). Cluster Center Initialization Using Hierarchical Two-Division of a Data Set along Each Dimension. In: Jin, D., Lin, S. (eds) Advances in Computer Science and Information Engineering. Advances in Intelligent and Soft Computing, vol 168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30126-1_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-30126-1_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30125-4
Online ISBN: 978-3-642-30126-1
eBook Packages: EngineeringEngineering (R0)