A Dynamic Adaptive Self-Organising Hybrid Model for Text Clustering

Clustering by document concepts is a powerful way of retrieving information from a large number of documents. This task in general does not make any assumption on the data distribution. In this paper, for this task we propose a new competitive Self-Organising (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. The hierarchical growing architecture is a useful facility for such a competitive neural model which is designed for text clustering. In this paper, we have presented a new type of self-organising dynamic growing neural network which can deal with the non-uniform data distribution and the non-stationary data sets and represent the inner data structure by a hierarchical view.

• Type: Conference
• Year: 2003
• Where: ICDM
• Authors: Chihli Hung, Stefan Wermter
• Keywords: Data Distribution, Data Mining, ICDM 2003, Non-stationary Data, Non-stationary Data Learning