fuzzy association rules, evluative linguistic expressions, syllogistic reasoning

The objective of this paper is to apply fuzzy natural logic together with the FuzzyGUHA method for analysis and linguistic characterization of scientific data. FuzzyGUHA is a tool for extracting linguistic association rules from data. Obtained associationsare IF-THEN rules composed of evaluative linguistic expressions, which allowthe quantities to be characterized with vague linguistic terms such as very small,big, medium etc. Originally, fuzzy GUHA provides several numerical indices ofrule quality, which may not be easily understandable for domain experts that are notfamiliar with GUHA association rules. Therefore, we show in this paper that the theoryof intermediate quantifiers (a constituent of fuzzy natural logic) can be applied to theresults in an automatic manner in order to obtain natural linguistic summarization.We also present an idea of how the theory of generalized Aristotles's syllogisms can beused for a detailed data analysis.