Linguistic Characterization of Natural Data by Applying Intermediate Quantifiers on Fuzzy Association Rules

The objective of this paper is to apply fuzzy natural logic together with the Fuzzy GUHA method for analysis andlinguistic characterization of scientific data. Fuzzy GUHA is a tool for extracting linguisticassociation rules from data. Obtained associations are IF-THEN rules composed of evaluativelinguistic expressions, which allow the quantities to be characterized with vague linguistic termssuch as ``very small'', ``big'', ``medium'' etc. Originally, fuzzy GUHA provides several numericalindices of rule 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 theory ofintermediate quantifiers (a constituent of fuzzy natural logic) can be applied to the resultsin an automatic manner in order to obtain natural linguistic summarization.We also present an idea of how the theory of generalized Aristotles's syllogismscan be used for a detailed data analysis. We also open the possibility to use fuzzy partial logicfor cases where some data are missing or undefined.