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Clustering quality and topology preservation in fast learning SOMs

Fiannaca, A., Di Fatta, G., Rizzo, R., Urso, A. and Gaglio, S. (2009) Clustering quality and topology preservation in fast learning SOMs. Neural Network World, 19 (5). pp. 625-639. ISSN 1210-0552

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Abstract/Summary

The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for data represented in multidimensional input spaces. In this paper, we describe Fast Learning SOM (FLSOM) which adopts a learning algorithm that improves the performance of the standard SOM with respect to the convergence time in the training phase. We show that FLSOM also improves the quality of the map by providing better clustering quality and topology preservation of multidimensional input data. Several tests have been carried out on different multidimensional datasets, which demonstrate better performances of the algorithm in comparison with the original SOM.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:15220
Uncontrolled Keywords:SOM, FLSOM, Clustering, ORGANIZING FEATURE MAPS
Additional Information:Special Issue on the 18th Int.l Conference on Artificial Neural Networks (ICANN’08)

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