A new modification of Kohonen neural network for VQ and clustering problems

Title: A new modification of Kohonen neural network for VQ and clustering problems
Creator: Mohebi, Ehsan; Bagirov, Adil
Date: 2013
Type: Text; Conference paper
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160939
Identifier: vital:12378
Abstract: Vector Quantization (VQ) and Clustering are significantly important in the field of data mining and pattern recognition. The Self Organizing Maps has been widely used for clustering and topology visualization. The topology of the SOM and its initial neurons play an important role in the convergence of the Kohonen neural network. In this paper, in order to improve the convergence of the SOM we introduce an algorithm based on the split and merging of clusters to initialize neurons. We also introduce a topology based on this initialization to optimize the vector quantization error. Such an approach allows one to find global or near global solution to the vector quantization and consequently clustering problem. The numerical results on 4 small to large real-world data sets are reported to demonstrate the performance of the proposed algorithm.
Relation: Proceedings of the 11-th Australasian Data Mining Conference (AusDM'13) Vol. 146, p. 81-88
Rights: This metadata is freely available under a CCO license
Subject: Self organizing maps; Clustering; Vector; Quantization; Split and merge algorithm.
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