Vague concept lattice reduction using granular computing and vague entropy

Prem Kumar Singh

Mathematics and Computers in Simulation (MATCOM), 2019, vol. 165, issue C, 56-73

Abstract: Recently, the calculus of Formal Concept Analysis (FCA) in the fuzzy setting is prolonged in bipolar fuzzy space for precise analysis of fuzzy attributes. In this process it is addressed that the attributes like bald (or tadpole) or not bald (not tadpole) cannot be defined through a precise or sharp boundaries. To deal with them, some evidence to support (i.e. true tA membership-values) or reject (i.e. false fA membership-values) the attributes is required in the given boundary 0≤tA+fA≤1. Hence, the proposed method tried to provide the mathematical algebra of vague concept lattice and its navigation at user defined granules with an illustrative example. In addition, the vague entropy measurement is also computed to validate the results.

Keywords: Concept lattice; Formal fuzzy concept; Granular computing; Vague graph; Vague entropy (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378475419300552
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:165:y:2019:i:c:p:56-73

DOI: 10.1016/j.matcom.2019.02.007

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().