Default Clustering with Conceptual Structures

Abstract : This paper describes a theoretical framework for inducing knowledge from incomplete data sets. The general framework can be used with any formalism based on a lattice structure. It is illustrated within two formalisms: the attribute-value formalism and Sowa’s conceptual graphs. The induction engine is based on a non-supervised algorithm called default clustering which uses the concept of stereotype and the new notion of default subsumption, inspired by the default logic theory. A validation using artificial data sets and an application concerning the extraction of stereotypes from newspaper articles are given at the end of the paper.
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Article dans une revue
Journal on Data Semantics, Springer, 2007, VIII, pp.1-25. <10.1007/978-3-540-70664-9_1>
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https://hal.archives-ouvertes.fr/hal-01185535
Contributeur : Lip6 Publications <>
Soumis le : jeudi 20 août 2015 - 15:24:23
Dernière modification le : lundi 29 mai 2017 - 14:22:13

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Julien Velcin, Jean-Gabriel Ganascia. Default Clustering with Conceptual Structures. Journal on Data Semantics, Springer, 2007, VIII, pp.1-25. <10.1007/978-3-540-70664-9_1>. <hal-01185535>

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