Performance analysis of Particle Swarm Optimization applied to unsupervised categorization of short texts

Leticia Cagnina , Diego Ingaramo , Marcelo Errecalde , Paolo Rosso


Nowadays there is a need to access to on line information such as abstracts,
news, opinions, evaluations of products, etc. That information is generally
available on the web as short texts. Previous works have demonstrated the effectiveness
of a discrete Particle Swarm Optimization algorithm, named CLUDIPSO, for
clustering small short-text corpora. This article presents a preliminary study about
the performance of CLUDIPSO on larger short-text corpora. The results were compared
with those of the most representative algorithms of the state-of-the-art in the
area. The experimental work gives strong evidence about the drawbacks of this
algorithm to manage larger corpora. With respect to this last aspect, some possible
reasons about the poor behavior of CLUDIPSO with larger short texts corpora are
discussed and some alternatives in order to solve the problems observed, are considered.

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