Representing attribute reduction and concepts in concept lattice using graphs (original) (raw)
Abstract
Concept lattice is an area of research which is based on a set-theoretical model for concepts and conceptual hierarchies. It is better for the studying of concept lattice to minimize the input data before revealing the construction of a concept lattice. Actually, this duty can be done by attribute reduction for a context. Graph is useful in data analysis since it gives us a visual trend on the behavior of our data points and allows us to test some laws in data analysis. This paper is a preliminary attempt to study how directed graph can be used on attribute reduction and conceptual construction in concept lattices. We investigate all the reducible attributes and concepts in a context with the aid of graph theory. For a context, we define a relevant graph on the set of attributes and, further, define a pre-weighted relevant graph. Afterward, using relevant graphs and pre-weighted relevant graphs with the method of deleting vertices in a directed graph, we find all the reducible attributes in a context. After that, we seek out all of concepts and the concept lattice for a given context. All these results may be not only used to improve the visibility and readability of attribute reduction in a context and the construction of concept lattice, but also broaden the applied range of directed graph such as in the field of attribute reduction.
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Acknowledgements
The author sincerely acknowledges the financial support from the Natural Science Foundation of China (61572011) and Natural Science Foundation of Hebei Province (A2013201119).
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- Department of Mathematics, Hebei University, Baoding, 071002, China
Hua Mao
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Correspondence toHua Mao.
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The authors declare that there is no conflict of interest regarding the publication of this paper.
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Communicated by A. Di Nola.
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Mao, H. Representing attribute reduction and concepts in concept lattice using graphs.Soft Comput 21, 7293–7311 (2017). https://doi.org/10.1007/s00500-016-2441-2
- Published: 21 November 2016
- Issue date: December 2017
- DOI: https://doi.org/10.1007/s00500-016-2441-2