A novel self-organising clustering model for time-event documents

Chihli Hung , Stefan Wermter

The Electronic Library, Volume 26, Number 2, pages 260--272, doi: 10.1108/02640470810864145 - Apr 2008

Associated documents :

Neural document clustering techniques, e.g., self-organising map (SOM) or growing neural gas (GNG), usually assume that textual information is stationary on the quantity. However, the quantity of text is ever-increasing. We propose a novel dynamic adaptive self-organising hybrid (DASH) model, which adapts to time-event news collections not only to the neural topological structure but also to its main parameters in a non-stationary environment.

@Article{HW08, 
 	 author =  {Hung, Chihli and Wermter, Stefan},  
 	 title = {A novel self-organising clustering model for time-event documents}, 
 	 journal = {The Electronic Library},
 	 number = {2},
 	 volume = {26},
 	 pages = {260--272},
 	 year = {2008},
 	 month = {Apr},
 	 publisher = {Emerald Insight},
 	 doi = {10.1108/02640470810864145}, 
 }