Towards a Text Mining Methodology Using Association Rules Extraction

Hacène Cherfi 1 Amedeo Napoli 1 Yannick Toussaint 1
1 ORPAILLEUR - Knowledge representation, reasonning
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : 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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Submitted on : Wednesday, October 19, 2005 - 4:16:51 PM
Last modification on : Thursday, January 11, 2018 - 6:19:53 AM

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Hacène Cherfi, Amedeo Napoli, Yannick Toussaint. Towards a Text Mining Methodology Using Association Rules Extraction. Soft Computing, Springer Verlag, 2006, A Fusion of Foundations, Methodologies and Applications, 10 (5), pp.431--441. ⟨10.1007/s00500-005-0504-x⟩. ⟨inria-00000460⟩

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