Similar Vague Concepts Selection Using Their Euclidean Distance at Different Granulation

Abstract

Recently, the calculus of fuzzy concept lattice is extensively studied with interval-valued and bipolar fuzzy set for the precise representation of vagueness in cognitive concept learning. In this process, selecting some of the semantic similar (or interesting) vague concept at user required granulation is addressed as one of the major issues. To conquer this problem, current paper aims at analyzing the cognitive concept learning based on the properties of vague graph, concept lattice and granular computing within (m × m) computational time. To mimic with the cognitive computing and its contextual data sets, two methods are proposed for the precise representation of human cognition based on the evidence to accept or reject the attributes using the calculus of vague set. The hierarchical order visualization among the extracted cognitive vague concept is shown through calculus of concept lattice for adequate description of their textual syntax analysis. In addition, another method is proposed to improve the quality of sentic (or cognitive) reasoning using semantics relationship among the discovered vague concepts at user defined information granulation. The obtained results from the proposed methods approve that the vague concept lattice and its reduction at user defined granulation provides an alternative way to analyze the cognitive contextual data set with an improved description of vague attributes “tall” and “young.” Euclidean distance provides a way to select some of the interesting vague concepts based on their semantic similarity. The obtained results from both of the proposed methods correspond to each other which validate the results. This paper establishes that cognitive contextual data set can be processed through the calculus of vague concept lattice, and granular computing. This gives an alternative and compact visualization of discovered patterns from the cognitive data set when compared to its numerical representation. In this way, the proposed method provides an adequate analysis for cognitive concept learning when compared to any of the available approaches. In addition, the proposed method provides various ways to select some of the interesting cognitive vague concepts at user defined granulation for their Euclidean distance within (m × m) computational time. However, the proposed method is unable to measure the fluctuation in uncertainty for the cognitive context at given phase of time. In the future, the author will focus on conquering this research problem with an illustrative example.