Abstract
Knowledge Discovery in Databases (KDD) systems are basically designed to extract knowledge nuggets from data, i.e. a very precise and hidden knowledge, rather than to provide a global view on database. Moreover, knowledge representation is often unintelligible for the user, such that a post-processing visualization step is necessary. Therefore, we propose a fuzzy-based summarization system named S AINTETlQ, providing different levels of summaries covering all the database. Summaries are output concepts of an incremental conceptual clustering algorithm performed on database records. Concept formation is the fundamental activity which structures objects into a concise form of knowledge that can be efficiently used in the future. It includes the classification of new objects based on a subset of their properties (the prediction ability), as well as the qualitative understanding of those objects based on the generated knowledge (the observation ability). In our approach, database records could be either crisp or fuzzy—imprecise, uncertain or missing. Their representation is then extended to fuzzy sets. Moreover, fuzzy background knowledge represented by fuzzy relational thesauri (FRT) on each attribute is essential to the generalization step of the system. Indeed, this fuzzy-based domain knowledge allows us to induce higher-level intents of concepts, representing part of the database. FRT are both built a priori on numerical and nominal attributes by domain experts, and coupled with a fuzzy discretization process performed on numerical attributes with Zadeh's linguistic variables. Using background knowledge into a concept learning process presents the Data Mining II, C.A. Brebbia & N.F.F. Ebecken (Editors) © 2000 WIT Press, www.witpress.com, ISBN 1-85312-821-X
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