Comprehensiveness and interpretability of linguistic data summaries: A natural language focused perspective

We consider the important problem of comprehensiveness of linguistic data summaries equated with linguistically quantified propositions in Zadeh's sense. Motivated by Michal-ski's [29] seminal approach to the comprehensiveness of data mining and machine learning results, with a clear emphasis on natural language, we advocate the use of linguistic summaries which provide a new quality and an exceptional human consistency and comprehensiveness. Extending our previous works, we first relate our approach to some related results on the interpretability and comprehensiveness of fuzzy rule bases, both with respect to structural and semantical complexity. We show the use of a fuzzy querying interface as not only an approach that is effective and efficient but which provides an exceptional comprehensiveness through its highly human consistent HCI (human computer interface). We emphasize a psychological and cognitive aspect of comprehensibility and interpretability analyses. We advocate the use of human consistent methods based on natural language. We indicate a possibility of using quantitative evaluations. We illustrate our analysis by two examples related to the linguistic summarization of both static and dynamic data in the area of innovation and Web log analyses, and justify the results obtained by domain experts positive assessments. In general, we propose a synergistic combination of formal and natural language based methods to solve the inherently human specific problem of comprehensiveness and interpretability.