On document classification with self-organising maps

Authors:
Jyri Saarikoski;Kalervo Järvelin;Jorma Laurikkala;Martti Juhola
Affiliations:
Department of Computer Sciences, University of Tampere, Finland;Department of Information Studies, University of Tampere, Finland;Department of Computer Sciences, University of Tampere, Finland;Department of Computer Sciences, University of Tampere, Finland
Venue:
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Year:
2009

Citing 10
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Abstract

This research deals with the use of self-organising maps for the classification of text documents. The aim was to classify documents to separate classes according to their topics. We therefore constructed self-organising maps that were effective for this task and tested them with German newspaper documents. We compared the results gained to those of k nearest neighbour searching and k-means clustering. For five and ten classes, the self-organising maps were better yielding as high average classification accuracies as 88-89%, whereas nearest neighbour searching gave 74-83% and k-means clustering 72- 79% as their highest accuracies.