A New Web Text Clustering Algorithm Based on DFSSM

Abstract

A new algorithm of Web text clustering mining is presented, which is based on the Discovery Feature Sub-space Model (DFSSM). This algorithm includes the training stage of SOM and the clustering stage, which characterizes self-stability and powerful antinoise ability. It can distinguishes the most meaningful features from the Concept Space without the evaluation function. we have applied the algorithm to the modern long-distance education. Through the analysis of the experimental results, it is obvious that this algorithm can effective help users to get valuable information from WWW quickly.