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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2017

25.03.2016 | Original Article

Multi-scaled concept lattices based on neighborhood systems

verfasst von: Li Ma, Ju-Sheng Mi, Bin Xie

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2017

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Abstract

Formal concept analysis (FCA) has been applied to many fields as an effective tool for data analysis and knowledge discovery. In fact, the problem of obtaining a concept lattice of appropriate complexity and size is one of the most important problems of FCA. In this paper, based on a kind of Galois connection via a concept of inclusion degree using a special neighborhood system, we propose a multi-scaled concept lattice. The presented method can effectively reduce the number of concepts, while conserving the main formal structure. A formal context can be converted into an induced context through a kind of inclusion degree which is used to cope with a special covering of the objects set. Moreover, we show that the concept lattice produced by the original context is equal to the concept lattice produced by the induced context. Finally, the multi-scaled concept lattice determined by an inclusion degree is constructed from the induced context.
Vorheriger Artikel Attributes reduction and rules acquisition in an lattice-valued information system with fuzzy decision
Nächster Artikel Cognitive concept learning from incomplete information

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Metadaten
Titel
Multi-scaled concept lattices based on neighborhood systems
verfasst von
Li Ma
Ju-Sheng Mi
Bin Xie
Publikationsdatum
25.03.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2017
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0521-3

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