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.
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Errecalde, M., Ingaramo, D., Rosso, P. (2010). ITSA ⋆ : An Effective Iterative Method for Short-Text Clustering Tasks. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13022-9_55
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DOI: https://doi.org/10.1007/978-3-642-13022-9_55
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