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Communication Dans Un Congrès Année : 2003

Towards a Text Mining Methodology Using Frequent Itemsets and Association Rule Extraction

Résumé

This paper proposes a methodology for text mining relying on the classical knowledge discovery loop, with a number of adaptations. First, texts are indexed and prepared to be processed by frequent itemset levelwise search. Association rules are then extracted and interpreted, with respect to a set of quality measures and domain knowledge, under the control of an analyst. The article includes an experimentation on a real-world text corpus holding on molecular biology.
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Dates et versions

inria-00107723 , version 1 (19-10-2006)

Identifiants

  • HAL Id : inria-00107723 , version 1

Citer

Hacène Cherfi, Amedeo Napoli, Yannick Toussaint. Towards a Text Mining Methodology Using Frequent Itemsets and Association Rule Extraction. Journées d'informatique Messine - JIM'03, E. SanJuan, Sep 2003, Metz, France, pp.285--294. ⟨inria-00107723⟩
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