Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Self-Organizing Maps
Document organization using Kohonen's algorithm
Information Processing and Management: an International Journal
Text Retrieval Using Self-Organized Document Maps
Neural Processing Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Mining massive document collections by the WEBSOM method
Information Sciences: an International Journal - Special issue: Soft computing data mining
Classifying Amharic news text using self-organizing maps
Semitic '05 Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages
On document classification with self-organising maps
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Unsupervised text classification using kohonen's self organizing network
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Theoretical aspects of mapping to multidimensional optimal regions as a multi-classifier
Intelligent Data Analysis
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This paper focuses on the use of self-organising maps, also known as Kohonen maps, for the classification task of text documents. The aim is to effectively and automatically classify documents to separate classes based on their topics. The classification with self-organising map was tested with three data sets and the results were then compared to those of six well known baseline methods: k-means clustering, Ward's clustering, k nearest neighbour searching, discriminant analysis, Naïve Bayes classifier and classification tree. The self-organising map proved to be yielding the highest accuracies of tested unsupervised methods in classification of the Reuters news collection and the Spanish CLEF 2003 news collection, and comparable accuracies against some of the supervised methods in all three data sets.