A document clustering algorithm for discovering and describing topics

被引：26

作者：

Anaya-Sanchez, Henry ^{[1
]}

Pons-Porrata, Aurora ^{[2
]}

Berlanga-Llavori, Rafael ^{[1
]}

机构：

[1] Univ Jaume 1, Dept Languages & Comp Syst, Castellon de La Plana, Spain

[2] Univ Oriente, Ctr Pattern Recognit & Data Min, Santiago De Cuba, Cuba

来源：

PATTERN RECOGNITION LETTERS | 2010年 / 31卷 / 06期

关键词：

Document clustering; Topic discovery; Topic description;

D O I：

10.1016/j.patrec.2009.11.013

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we introduce a new clustering algorithm for discovering and describing the topics comprised in a text collection. Our proposal relies on both the most probable term pairs generated from the collection and the estimation of the topic homogeneity associated to these pairs Topics and their descriptions are generated from those term pairs whose support sets are homogeneous enough for representing collection topics Experimental results obtained over three benchmark text collections demonstrate the effectiveness and utility of this new approach (C) 2009 Published by Elsevier B V

引用

页码：502 / 510

页数：9

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