Artículo
An efficient Particle Swarm Optimization approach to cluster short texts
Fecha de publicación:
05/2014
Editorial:
Elsevier
Revista:
Information Sciences
ISSN:
0020-0255
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Short texts such as evaluations of commercial products, news, FAQ’s and scientific abstracts are important resources on the Web due to the constant requirements of people to use this on line information in real life. In this context, the clustering of short texts is a significant analysis task and a discrete Particle Swarm Optimization (PSO) algorithm named CLUDIPSO has recently shown a promising performance in this type of problems. CLUDIPSO obtained high quality results with small corpora although, with larger corpora, a significant deterioration of performance was observed. This article presents CLUDIPSO★, an improved version of CLUDIPSO, which includes a different representation of particles, a more efficient evaluation of the function to be optimized and some modifications in the mutation operator. Experimental results with corpora containing scientific abstracts, news and short legal documents obtained from the Web, show that CLUDIPSO★ is an effective clustering method for short-text corpora of small and medium size.
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Articulos(CCT - SAN LUIS)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - SAN LUIS
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - SAN LUIS
Citación
Cagnina, Leticia Cecilia; Errecalde, Marcelo Luis; Ingaramo, Diego; Rosso, Paolo; An efficient Particle Swarm Optimization approach to cluster short texts; Elsevier; Information Sciences; 265; 5-2014; 36-49
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