Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
536585 | Pattern Recognition Letters | 2010 | 9 Pages |
Abstract
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Henry Anaya-Sánchez, Aurora Pons-Porrata, Rafael Berlanga-Llavori,