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An efficient Particle Swarm Optimization approach to cluster short texts

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An efficient Particle Swarm Optimization approach to cluster short texts

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dc.contributor.author Cagnina, Leticia es_ES
dc.contributor.author Errecalde, Marcelo es_ES
dc.contributor.author Ingaramo, Diego es_ES
dc.contributor.author Rosso, Paolo es_ES
dc.date.accessioned 2015-05-04T11:13:47Z
dc.date.available 2015-05-04T11:13:47Z
dc.date.issued 2014-05
dc.identifier.issn 0020-0255
dc.identifier.uri http://hdl.handle.net/10251/49604
dc.description This is the author’s version of a work that was accepted for publication in Information Sciencies. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, VOL 265, MAY 1 2014 DOI 10.1016/j.ins.2013.12.010. es_ES
dc.description.abstract 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. (C) 2013 Elsevier Inc. All rights reserved. es_ES
dc.description.sponsorship The research work is partially funded by the European Commission as part of the WIQ-EI IRSES research project (Grant No. 269180) within the FP 7 Marie Curie People Framework and it has been developed in the framework of the Microcluster VLC/Campus (International Campus of Excellence) on Multimodal Intelligent Systems. The research work of the first author is partially funded by the program PAID-02-10 2257 (Universitat Politecnica de Valencia) and CONICET (Argentina). en_EN
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Information Sciences es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Clustering es_ES
dc.subject Short-text corpora es_ES
dc.subject Particle Swarm Optimization es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title An efficient Particle Swarm Optimization approach to cluster short texts es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.ins.2013.12.010
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/269180/EU/Web Information Quality Evaluation Initiative/ en_EN
dc.relation.projectID info:eu-repo/grantAgreement/UPV//PAID-02-10 2257/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Cagnina, L.; Errecalde, M.; Ingaramo, D.; Rosso, P. (2014). An efficient Particle Swarm Optimization approach to cluster short texts. Information Sciences. 265:36-49. https://doi.org/10.1016/j.ins.2013.12.010 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.ins.2013.12.010 es_ES
dc.description.upvformatpinicio 36 es_ES
dc.description.upvformatpfin 49 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 265 es_ES
dc.relation.senia 285851
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina es_ES


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