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. 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, nonstationary 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. We have presented a new type of self-organising dynamic growing neural network which can deal with the nonuniform data distribution and the nonstationary data sets and represent the inner data structure by a hierarchical view.