Unsupervised neural networks of topographic and gas families documentary data classification

Shadi Al Shehabi 1 Jean-Charles Lamirel 1
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : The unsupervised neural networks are excellent tools for the analysis of high-dimensional input data as in data mining applications. In this paper different methods of data clustering are considered as the self-organizing map (SOM), neural gas (NG) and growing neural gas (GNG).This paper demonstrates, in one side, the efficiency of a viewpoint-oriented-analysis as compared to a global analysis, and in the other side, it compare these three unsupervised neural clustering methods as classifier for documentary data. For that, two quality criteria are taken into account for quality evaluation. These criteria are used as well for highlighting the clustering methods internal operation.
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Communication dans un congrès
The 8th World Multi-Conference on Systemics, Cybernetics and Informatics - WMSCI'04, 2004, Orlando, USA, 2004
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Soumis le : mardi 26 septembre 2006 - 10:14:48
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48

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  • HAL Id : inria-00100156, version 1

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Shadi Al Shehabi, Jean-Charles Lamirel. Unsupervised neural networks of topographic and gas families documentary data classification. The 8th World Multi-Conference on Systemics, Cybernetics and Informatics - WMSCI'04, 2004, Orlando, USA, 2004. 〈inria-00100156〉

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