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ICANNGA
2009
Springer

On Document Classification with Self-Organising Maps

13 years 1 months ago
On Document Classification with Self-Organising Maps
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 7279% as their highest accuracies.
Jyri Saarikoski, Kalervo Järvelin, Jorma Laur
Added 18 Feb 2011
Updated 18 Feb 2011
Type Journal
Year 2009
Where ICANNGA
Authors Jyri Saarikoski, Kalervo Järvelin, Jorma Laurikkala, Martti Juhola
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