A Self-Organising Hybrid Model for Dynamic Text Clustering

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.

@InProceedings{HW03, 
 	 author =  {Hung, Chihli and Wermter, Stefan},  
 	 title = {A Self-Organising Hybrid Model for Dynamic Text Clustering}, 
 	 booktitle = {Proceedings of the The Twenty-third SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence},
 	 editors = {Ellis, Richard; Macinosh, Ann},
 	 number = {},
 	 volume = {},
 	 pages = {141--154},
 	 year = {2003},
 	 month = {Dec},
 	 publisher = {Springer},
 	 doi = {0.1007/978-0-85729-412-8_11}, 
 }