Fuzzy natural logic, Linguistic associations mining, Intermediate quantifiers, Generalized square of opposition, Fuzzy GUHA

The aim of this paper is to apply main theories of fuzzy natural logic together with fuzzy GUHA method for a linguistic characterization of relationships in data. Namely, we utilize the theoryof intermediate quantifiers, which provides mathematical interpretation of natural languageexpressions describing quantity such as ``Almost all'', ``Few'' etc., to describe relationshipsin data using vague terms that are natural in human expression. We provide an algorithm for computation of truth degrees of expressions containing such quantifiers. Moreover, we discuss somebasic properties of intermediate quantifiers (contraries, contradictories, sub-contraries andsub-alterns), which formulate the graded Peterson's square of opposition, and which can be used toinfer new expressions from existing ones.