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Title: Data clustering with cluster size constraints using a modified k-means algorithm
Authors: Ganganath, N
Cheng, CT 
Tse, CK 
Keywords: Constrained clustering
Data clustering
Data mining
Size constraints
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: 2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 13-15 Oct. 2014, Shanghai, China, p. 158-161 How to cite?
Abstract: Data clustering is a frequently used technique in finance, computer science, and engineering. In most of the applications, cluster sizes are either constrained to particular values or available as prior knowledge. Unfortunately, traditional clustering methods cannot impose constrains on cluster sizes. In this paper, we propose some vital modifications to the standard k-means algorithm such that it can incorporate size constraints for each cluster separately. The modified k-means algorithm can be used to obtain clusters in preferred sizes. A potential application would be obtaining clusters with equal cluster size. Moreover, the modified algorithm makes use of prior knowledge of the given data set for selectively initializing the cluster centroids which helps escaping from local minima. Simulation results on multidimensional data demonstrate that the k-means algorithm with the proposed modifications can fulfill cluster size constraints and lead to more accurate and robust results.
ISBN: 978-1-4799-6235-8
DOI: 10.1109/CyberC.2014.36
Rights: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication Ganganath, N., Cheng, C. -., & Tse, C. K. (2014). Data clustering with cluster size constraints using a modified k-means algorithm. Paper presented at the Proceedings - 2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2014, 158-161 is available at 10.1109/CyberC.2014.36
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