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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 11/2019

20.09.2019 | Original Article

A theoretical study on object-oriented and property-oriented multi-scale formal concept analysis

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 11/2019

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Abstract

In traditional formal concept analysis, the attributes in the formal context are considered fixed. However, in the real world data set, attributes always have different levels of granularity, correspondingly, the derived concept lattice may reveal different information and patterns. Therefore, the capability to change the level of granularity of an attribute in formal concept analysis to capture relevant patterns in data is a natural requirement. In this paper, a theoretical study has been undertaken in multi-scale formal contexts, where attributes with different levels of granularity possess different attribute values. Two types of formal concepts, i.e., object-oriented and property-oriented multi-scale concepts, are introduced and studied in detail. The collection of object-oriented concept lattices and property-oriented concept lattices can be obtained at different granularity levels of attributes. It has been shown that the set of extents in the derived concept lattices increases when we choose to use a finer level of granularity. Moreover, a corresponding bidirectional approach to concept construction(i.e., from coarser to finer and from finer to coarser, respectively) is exhibited, and some characterization theorems have been obtained.
Vorheriger Artikel A knowledge acquisition method based on concept lattice and inclusion degree for ordered information systems
Nächster Artikel Neural variational collaborative filtering with side information for top-K recommendation

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Metadaten
Titel
A theoretical study on object-oriented and property-oriented multi-scale formal concept analysis
Publikationsdatum
20.09.2019
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 11/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-019-01015-3

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