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.