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.

• Type: Journal
• Year: 2009
• Where: ICANNGA
• Authors: Jyri Saarikoski, Kalervo Järvelin, Jorma Laurikkala, Martti Juhola
• Keywords: Algorithms, Documents, ICANNGA 2009, K-means, Self-organising Maps