Abstract
In this paper we describe a Genetic Algorithm for document clustering that includes a sampling technique to reduce computation time. This algorithm calculates an approximation of the optimum k value, and solves the best grouping of the documents into these k clusters. We evaluate this algorithm with sets of documents that are the output of a query in a search engine. Two types of experiment are carried out to determine: (1) how the genetic algorithm works with a sample of documents, (2) which document features lead to the best clustering according to an external evaluation. On the one hand, our GA with sampling performs the clustering in a time that makes interaction with a search engine viable. On the other hand, our GA approach with the representation of the documents by means of entities leads to better results than representation by lemmas only.
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Casillas, A., de Lena, M.T.G., Martínez, R. (2004). Sampling and Feature Selection in a Genetic Algorithm for Document Clustering. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2004. Lecture Notes in Computer Science, vol 2945. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24630-5_74
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DOI: https://doi.org/10.1007/978-3-540-24630-5_74
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