Topic Extraction with AGAPE

Abstract : This paper uses an optimization approach to address the problem of conceptual clustering. The aim of AGAPE, which is based on the tabu-search meta-heuristic using split, merge and a special “k-means” move, is to extract concepts by optimizing a global quality function. It is deterministic and uses no a priori knowledge about the number of clusters. Experiments carried out in topic extraction show very promising results on both artificial and real datasets.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01336130
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Submitted on : Wednesday, June 22, 2016 - 4:17:45 PM
Last modification on : Thursday, March 21, 2019 - 2:43:07 PM

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Julien Velcin, Jean-Gabriel Ganascia. Topic Extraction with AGAPE. The International Conference on Advanced Data Mining and Applications (ADMA), Aug 2007, Harbin, China. pp.377-388, ⟨10.1007/978-3-540-73871-8_35⟩. ⟨hal-01336130⟩

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