On Document Classification with Self-Organising Maps

被引：0

作者：

Saarikoski, Jyri ^{[1
]}

Jarvelin, Kalervo ^{[2
]}

Laurikkala, Jorma ^{[1
]}

Juhola, Martti ^{[1
]}

机构：

[1] Univ Tampere, Dept Comp Sci, Tampere 33014, Finland

[2] Univ Tampere, Dept Informat Studies, Tampere 33014, Finland

来源：

ADAPTIVE AND NATURAL COMPUTING ALGORITHMS | 2009年 / 5495卷

基金：

芬兰科学院;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：140 / +

页数：2

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