Self organization of a massive document collection

被引：521

作者：

Kohonen, T ^{[1
]}

Kaski, S ^{[1
]}

Lagus, K ^{[1
]}

Salojärvi, J ^{[1
]}

Honkela, J ^{[1
]}

Paatero, V ^{[1
]}

Saarela, A ^{[1
]}

机构：

[1] Aalto Univ, Neural Networks Res Ctr, FIN-02150 Espoo, Finland

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2000年 / 11卷 / 03期

基金：

芬兰科学院;

关键词：

data mining; exploratory data analysis; knowledge discovery; large databases; parallel implementation; random projection; self-organizing map (SOM); textual documents;

D O I：

10.1109/72.846729

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article describes the implementation of a system that is able to organize vast document collections according to textual similarities. It is based on the self-organizing map (SOM) algorithm. As the feature vectors for the documents statistical representations of their vocabularies are used. The main goal in our work: has been to scale up the SOM algorithm to be able to deal with large amounts of high-dimensional data. In a practical experiment we mapped 6 840 568 patent abstracts onto a 1 002 240-node SOM, As the feature vectors we used 500-dimensional vectors of stochastic figures obtained as random projections of weighted word histograms.

引用

页码：574 / 585

页数：12

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