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

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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