- Title
- Constrained self organizing maps for data clusters visualization
- Creator
- Mohebi, Ehsan; Bagirov, Adil
- Date
- 2016
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/101161
- Identifier
- vital:10658
- Identifier
- ISSN:13704621 (ISSN)
- Abstract
- High dimensional data visualization is one of the main tasks in the field of data mining and pattern recognition. The self organizing maps (SOM) is one of the topology visualizing tool that contains a set of neurons that gradually adapt to input data space by competitive learning and form clusters. The topology preservation of the SOM strongly depends on the learning process. Due to this limitation one cannot guarantee the convergence of the SOM in data sets with clusters of arbitrary shape. In this paper, we introduce Constrained SOM (CSOM), the new version of the SOM by modifying the learning algorithm. The idea is to introduce an adaptive constraint parameter to the learning process to improve the topology preservation and mapping quality of the basic SOM. The computational complexity of the CSOM is less than those with the SOM. The proposed algorithm is compared with similar topology preservation algorithms and the numerical results on eight small to large real-world data sets demonstrate the efficiency of the proposed algorithm. © 2015, Springer Science+Business Media New York.
- Publisher
- Springer New York LLC
- Relation
- Neural Processing Letters Vol. 43, no. 3 (2016), p. 849-869
- Rights
- Copyright © 2015, Springer Science+Business Media New York.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 1702 Cognitive Science; Clustering; CSOM; Self organizing maps; SOM learning algorithm; Vector quantization
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