This paper proposes an ant colony optimization (ACO) algorithm to deal with fuzzy document clustering problems. A specialized glossary and a thesaurus are used in order to extract features of the documents and to obtain a language-independent vector representation that can be used to measure similarities between documents written in different languages. The pheromone trails obtained in the ACO process are used to determine membership values in a fuzzy clustering. To illustrate the behavior of the algorithm, it was applied to a corpus of bilingual documents in different areas of economic and management.
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