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- W6664145 abstract "AbstractRelations play a crucial role in rough set theory and KDD. Typical applications of rough set methods in KDD are based on a given relation or a given family of relations (e.g. indiscernibility relation, tolerance relation, etc.). In this paper we propose a general framework for concept discovery from data using two-level learning approach. The first level responds for approximation of an unknown or a partially defined relation, and the second level uses the approximated relation to approximate some more complex concepts. We consider two types of relations including similarity and preference relations. We discuss the rough-set based methods for relation discovery and present experimental results for some complex data sets.KeywordsRelation ApproximationDecision TableRanking ListTarget ConceptRanking AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W6664145 date "2012-01-01" @default.
- W6664145 modified "2023-09-27" @default.
- W6664145 title "A Rough Set Approach to Knowledge Discovery by Relation Approximation" @default.
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- W6664145 doi "https://doi.org/10.1007/978-3-642-31709-5_34" @default.
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