Self-organizing maps of massive document collections

被引：10

作者：

Kohonen, T ^{[1
]}

机构：

[1] Aalto Univ, Neural Networks Res Ctr, FIN-02015 Espoo, Finland

来源：

IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL II | 2000年

关键词：

D O I：

10.1109/IJCNN.2000.857865

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Huge document collections can be organized according to textual similarities by the Self-Organizing Map (SOM) algorithm, when statistical representations of the textual contents are used as the feature vectors of the documents. In a practical experiment we mapped 6,840,568 patent abstracts onto a 1,002,240-node SOM. For the feature vectors we selected 500-dimensional random projections of the weighted word histograms.

引用

页码：3 / 9

页数：7

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