Vague concept lattice reduction using granular computing and vague entropy

• Published in: Mathematics and computers in simulation 2019-11, Vol.165, p.56-73
• Main Author: Singh, Prem Kumar
• Format: Article
• Language: English
• Subjects: Concept lattice; Formal fuzzy concept; Granular computing; Vague entropy; Vague graph
• ISSN: 0378-4754; 1872-7166
• DOI: 10.1016/j.matcom.2019.02.007

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. •The properties of vague set is introduced to handle the uncertainty in data sets beyond the interval-valued fuzzy set.•The granular based decomposition of vague context and its processing is also discussed to solve the particular problem and its complexity.•The vague entropy method is also utilized to measure the randomness in vague attributes.•A real life example is also illustrated for applications of the proposed method to analyze the acceptance and rejection of submitted manuscript in Scopus Indexed journal based on chosen parameters.