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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Abstract-In this work, we investigate the relative hardness of shorttext corpora in clustering problems and how this hardness relates to traditional similarity measures. Our approach basically attempts to establish a connection between the hardness of a corpus and the precision level exhibited by similarity measures, according to the results obtained with different cluster validity measures on the "ideal" clustering of each corpus. Moreover, we also propose a new validity measure, named contiguity error that allowed us to observe this connection in a consistent way in all the collections considered.

Abstract. The current tendency for people to use very short documents, e.g. blogs, text-messaging, news and others, has produced an increasing interest in automatic processing techniques which are able to deal with documents with these characteristics. In this context, "short-text clustering" is a very important research field where new clustering algorithms have been recently proposed to deal with this difficult problem. In this work, ITSA , an iterative method based on the bio-inspired method PAntSA is proposed for this task. ITSA takes as input the results obtained by arbitrary clustering algorithms and refines them by iteratively using the PAntSA algorithm. The proposal shows an interesting improvement in the results obtained with different algorithms on several short-text collections. However, ITSA can not only be used as an effective improvement method. Using random initial clusterings, ITSA outperforms well-known clustering algorithms in most of the experimental instances.