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Mathematical Problems in Engineering
Volume 2016, Article ID 9324793, 7 pages
Research Article

Neural Gas Clustering Adapted for Given Size of Clusters

Department of Applied Informatics and Mathematics, University of SS. Cyril and Methodius, J. Herdu 2, 917 01 Trnava, Slovakia

Received 19 April 2016; Revised 8 September 2016; Accepted 26 October 2016

Academic Editor: Dan Simon

Copyright © 2016 Iveta Dirgová Luptáková et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Clustering algorithms belong to major topics in big data analysis. Their main goal is to separate an unlabelled dataset into several subsets, with each subset ideally characterized by some unique characteristic of its data structure. Common clustering approaches cannot impose constraints on sizes of clusters. However, in many applications, sizes of clusters are bounded or known in advance. One of the more recent robust clustering algorithms is called neural gas which is popular, for example, for data compression and vector quantization used in speech recognition and signal processing. In this paper, we have introduced an adapted neural gas algorithm able to accommodate requirements for the size of clusters. The convergence of algorithm towards an optimum is tested on simple illustrative examples. The proposed algorithm provides better statistical results than its direct counterpart, balanced k-means algorithm, and, moreover, unlike the balanced k-means, the quality of results of our proposed algorithm can be straightforwardly controlled by user defined parameters.