Comparison of neural models for document clustering

被引：14

作者：

Guerrero-Bote, VP ^{[1
]}

López-Pujalte, C

de Moya-Anegón, F

Herrero-Solana, V

机构：

[1] Univ Extremadura, Fac Bibliotecon & Documentac, E-06071 Badajoz, Spain

[2] Univ Granada, Fac Bibliotecon & Documentac, E-18071 Granada, Spain

来源：

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING | 2003年 / 34卷 / 2-3期

关键词：

artificial neural networks; Kohonen networks; document clustering; adaptive resonance theory;

D O I：

10.1016/j.ijar.2003.07.012

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We compared the application of different algorithms to document clustering. The algorithms studied were Fuzzy C-Means, Fuzzy ART, Fuzzy ART for Fuzzy Clusters, Fuzzy Max-Min, and the Kohonen neural network (only the first is not a neural network). We generated a testbed from LISA, using some of the descriptors corresponding to the different records for the comparison of the results. The best results were found with Kohonen's algorithm which also organizes the clusters topologically. We end by discussing in more detail the possibilities offered by Kohonen's algorithm. (C) 2003 Elsevier Inc. All rights reserved.

引用

页码：287 / 305

页数：19

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