- -

An efficient Particle Swarm Optimization approach to cluster short texts

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

An efficient Particle Swarm Optimization approach to cluster short texts

Show full item record

Cagnina, L.; Errecalde, M.; Ingaramo, D.; Rosso, P. (2014). An efficient Particle Swarm Optimization approach to cluster short texts. Information Sciences. 265:36-49. doi:10.1016/j.ins.2013.12.010

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/49604

Files in this item

Item Metadata

Title: An efficient Particle Swarm Optimization approach to cluster short texts
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
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 ...[+]
Subjects: Clustering , Short-text corpora , Particle Swarm Optimization
Copyrigths: Reserva de todos los derechos
Source:
Information Sciences. (issn: 0020-0255 )
DOI: 10.1016/j.ins.2013.12.010
Publisher:
Elsevier
Publisher version: http://dx.doi.org/10.1016/j.ins.2013.12.010
Project ID: info:eu-repo/grantAgreement/EC/FP7/269180/EU
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.
Thanks:
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 ...[+]
Type: Artículo

This item appears in the following Collection(s)

Show full item record