Knowledge structure, knowledge granulation and knowledge distance in a knowledge base
International Journal of Approximate Reasoning
Computing with words in decision making: foundations, trends and prospects
Fuzzy Optimization and Decision Making
Notes on automatic music conversions
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Granular modelling of signals: A framework of Granular Computing
Information Sciences: an International Journal
Multigranulation rough sets: From partition to covering
Information Sciences: an International Journal
Building the fundamentals of granular computing: A principle of justifiable granularity
Applied Soft Computing
On Characterizing Hierarchies of Granulation Structures via Distances
Fundamenta Informaticae
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We are concerned with the granular representation of mappings (or experimental data) coming in the form R:R→[0,1] (for one-dimensional cases) and R:Rn→[0,1] (for multivariable cases) with R being a set of real numbers. As the name implies, a granular mapping is defined over information granules and maps them into a collection of granules expressed in some output space. The design of the granular mapping is discussed in the case of set and fuzzy set-based granulation. The proposed development is regarded as a two-phase process that comprises: 1) a definition of an interaction between information granules and experimental evidence or existing numeric mapping and 2) the use of these measures of interaction in building an explicit expression for the granular mapping. We show how to develop information granules in case of multidimensional numeric data by resorting to fuzzy clustering (fuzzy C-means). Experimental results serve as an illustration of the proposed approach.