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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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Submitted on : Thursday, August 20, 2015 - 3:24:23 PM
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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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