Abstract
A text clustering neural model, traditionally, is assumed to cluster static text information and represent its inner structure on a flat map. However, the quantity of text information is continuously growing and the relationships between them are usually complicated. Therefore, the information is not static and a flat map may be not enough to describe the relationships of input data. In this paper, for a real-world text clustering task we propose a new competitive Self-Organising Map (SOM) model, namely the Dynamic Adaptive Self-Organising Hybrid model (DASH). The features of DASH are a dynamic structure, hierarchical clustering, non-stationary data learning and parameter self-adjustment. All features are data-oriented: DASH adjusts its behaviour not only by modifying its parameters but also by an adaptive structure . We test the performance of our model using the larger new Reuters news corpus based on the criteria of classification accuracy and mean quantization error.
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Hung, C., Wermter, S. (2004). A Self-Organising Hybrid Model for Dynamic Text Clustering. In: Coenen, F., Preece, A., Macintosh, A. (eds) Research and Development in Intelligent Systems XX. SGAI 2003. Springer, London. https://doi.org/10.1007/978-0-85729-412-8_11
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DOI: https://doi.org/10.1007/978-0-85729-412-8_11
Publisher Name: Springer, London
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