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.

@InProceedings{HW03a, 
 	 author =  {Hung, Chihli and Wermter, Stefan},  
 	 title = {A Dynamic Adaptive Self-Organising Hybrid Model for Text Clustering}, 
 	 booktitle = {Proceedings of The Third IEEE International Conference on Data Mining},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {75--82},
 	 year = {2003},
 	 month = {},
 	 publisher = {IEEE},
 	 doi = {}, 
 }