Title :
A comparison of SOM based document categorization systems
Author :
Luo, X. ; Zincir-Heywood, A. Nur
Author_Institution :
Fac. of Comput. Sci., Dalhousie Univ., Halifax, NS, Canada
Abstract :
This paper describes the development and evaluation of two unsupervised learning mechanisms for solving the automatic document categorization problem. Both mechanisms are based on a hierarchical structure of self-organizing feature maps. Specifically, one architecture is based on the vector space model whereas the other one is based on a code-books model. Results show that the latter architecture performs better than the first one which is based on the quality of the returned clusters.
Keywords :
classification; pattern clustering; self-organising feature maps; unsupervised learning; document categorization systems; hierarchical structure; information retrieval systems; pattern clustering; self-organizing feature maps; unsupervised learning; vector space model; Clustering algorithms; Computer architecture; Computer science; Fingerprint recognition; Frequency; Information retrieval; Nearest neighbor searches; Organizing; Unsupervised learning; Vocabulary;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223678
