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.
Type de document :
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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Soumis le : jeudi 20 août 2015 - 15:24:23
Dernière modification le : jeudi 21 mars 2019 - 14:40:17



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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