Title :
Classifying News Corpus by self-organizing maps
Author :
Yanagida, T. ; Miura, Takao ; Shioya, Isamu
Author_Institution :
Dept. of Elect. & Elect. Engr., Hosei Univ., Tokyo, Japan
Abstract :
In this paper, we introduce extended self organization map (SOM), called k-propagated SOM (K-SOM, or SOM(k)), and discuss how to classify text documents. Also we discuss how we evaluate classification capabilities of points on SOM (K-SOM) maps. We discuss some experiments to Reuters News Corpus datasets and show the usefulness of K-SOM.
Keywords :
self-organising feature maps; text analysis; word processing; Reuters News Corpus dataset; k-propagated self-organizing map; text document classification; Data mining; Informatics; Principal component analysis; Self organizing feature maps; Singular value decomposition; Support vector machine classification; Support vector machines; Testing; Text categorization; Training data;
Conference_Titel :
Communications, Computers and signal Processing, 2003. PACRIM. 2003 IEEE Pacific Rim Conference on
Print_ISBN :
0-7803-7978-0
DOI :
10.1109/PACRIM.2003.1235902
