- Title
- Modified self-organising maps with a new topology and initialisation algorithm
- Creator
- Mohebi, Ehsan; Bagirov, Adil
- Date
- 2015
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/89525
- Identifier
- vital:9298
- Identifier
-
https://doi.org/10.1080/0952813X.2014.954278
- Identifier
- ISSN:0952-813x
- Abstract
- Mapping quality of the self-organising maps (SOMs) is sensitive to the map topology and initialisation of neurons. In this article, in order to improve the convergence of the SOM, an algorithm based on split and merge of clusters to initialise neurons is introduced. The initialisation algorithm speeds up the learning process in large high-dimensional data sets. We also develop a topology based on this initialisation to optimise the vector quantisation error and topology preservation of the SOMs. Such an approach allows to find more accurate data visualisation and consequently clustering problem. The numerical results on eight small-to-large real-world data sets are reported to demonstrate the performance of the proposed algorithm in the sense of vector quantisation, topology preservation and CPU time requirement. © 2014 Taylor & Francis.
- Publisher
- Taylor and Francis Ltd.
- Relation
- Journal of Experimental and Theoretical Artificial Intelligence Vol. 27, no. 3 (2015), p. 351-372
- Rights
- Copyright © 2014 Taylor & Francis
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 1702 Cognitive Science; Self-organising maps; SOM initialisation algorithm; SOM learning algorithm; SOM topology preservation; Vector quantisation; Algorithms; Data visualization; Learning algorithms; Self organizing maps; Topology; Virtual reality; Algorithm speed; Clustering problems; Large high-dimensional data; Learning process; Numerical results; Split-and-merge; Topology preservation; Clustering algorithms
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