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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Default Clustering with Conceptual Structures
verfasst von : Julien Velcin, Jean-Gabriel Ganascia
Erschienen in: Journal on Data Semantics VIII
Verlag: Springer Berlin Heidelberg
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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.