The fundamental feature of human-friendly decision-making models (such as those encountered in complex medical problems, economical or political systems, technical diagnostic of physical systems, etc.) is predominantly concerned with interpretability of resulting constructs. Interpretability comes hand in hand with the granular nature of conceptual entities which are sought as the generic building blocks of such decision models and directly support a logic nature of their processing. From the system development perspective, the interpretability begs for solutions to the fundamental problems which need to be fully addressed with this regard. These concern: (a) a construction of information granules (both one-dimensional as well as multivariable structures), and (b) exploitation of logic operators and aggregation operators that are carefully adjusted to cope with available experimental data.
In this study, we concentrate on the two design problems identified above and show how they could be efficiently handled by making use of the carefully crafted methodology of fuzzy sets. The design of information granules is discussed in the setting of fuzzy clustering where we envision an incorporation of the machinery of user feedback so that the information granules are formed both on a basis of available experimental evidence (numeric data) whose processing is cast in the framework of a navigation setup formed by the user/designer realized through the formation of the relevance feedback loop. The construction of logic operators aimed at the logic aggregation of information granules builds upon the available data while adhering to the principles of logic computing. Given this character of processing, we will be referring to these constructs as statistically grounded logic aggregators.