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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 10/2019

02.01.2019 | Original Article

Attribute reduction and rule acquisition of formal decision context based on object (property) oriented concept lattices

verfasst von: Keyun Qin, Bo Li, Zheng Pei

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 10/2019

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Abstract

The study of concept lattices, property oriented concept lattices and object oriented concept lattices provides complementary conceptual structures, which can be used to search, analyze and extract information from data sets. This paper is devoted to the study of rule acquisition and attribute reduction of formal decision context. Based on object oriented concepts and property oriented concepts, the notions of object oriented decision rules and property oriented decision rules are proposed. By using some equivalence relations on the set of extents of the related conditional concept lattices and decision concept lattices, the rule acquisition methods are presented. The attribute reduction approaches for formal decision context to preserve the object oriented decision rules and property oriented decision rules are put forward by using discernibility attributes.
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Metadaten
Titel
Attribute reduction and rule acquisition of formal decision context based on object (property) oriented concept lattices
verfasst von
Keyun Qin
Bo Li
Zheng Pei
Publikationsdatum
02.01.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 10/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-00907-0

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