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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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https://hal.archives-ouvertes.fr/hal-01185535
Contributor : Lip6 Publications <>
Submitted on : Thursday, August 20, 2015 - 3:24:23 PM
Last modification on : Friday, December 13, 2019 - 11:44:05 AM

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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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