Text Mining with Adaptive Neural Networks
نویسندگان
چکیده
Analysing high-dimensional data is a task where software tools can reasonably assist the data analyst, by visualising, and thereby uncovering, the inherent structure and topology of the data collection. Especially the kinds of tools that can produce results autonomously, i.e. unsupervised tools, are a goal; here, neural network models may be one solution. In the category of unsupervised neural network models, the ones based on the principles of the Self-Organizing Map have become quite popular. We have tested the applicability of adaptive unsupervised neural network models, speci cally of a model which was proposed just recently, the Adaptive Hierarchical Incremental Grid Growing, for free-text data in the domain of tourism: we have utilised the model to create a structured and hierarchically organised view of Austrian hotels. To be able to give a good analysis of the model's strength and weaknesses, we have furthermore compared the results with two other models, the standard Self-Organizing Map respectively the Growing Grid, and the Growing Hierarchical Self-Organizing Map.
منابع مشابه
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