Unified Adaptive Resonance Theory Neural Networks for Numeric and Text data

• Autores: Juthamas Choruengwiwat, Worapoj Kreesuradej
• Localización: International journal of the computer, the internet and management, ISSN 0858-7027, Vol. 13, Nº. 2 (OCT), 2005 (Ejemplar dedicado a: Suplemento 2: eIndustry 2005. Proceedings of the International Conference on Computer and Industrial Management), págs. 26-26
• Idioma: inglés
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• Resumen

  • This paper proposes a new neural network, called a unified adaptive resonance theory neural network, for clustering objects whose feature values may combine numeric and text data. The proposed model is based on the concepts of fuzzy ART neural networks and similarity measure of symbolic objects. The proposed model works directly on textual and numerical information without mapping objects onto some representations that have quantitative features. The inputs of the proposed neural network can directly receive both a textual value and a numerical value without mapping the text value into a numerical value. The preliminary experimental results show the unified adaptive resonance theory neural network that can correctly cluster data.