DCf: a double clustering framework for fuzzy information granulation

In this paper, we present a framework for extracting well-defined and semantically sound information granules. The framework is mainly centered on a double clustering process, hence, it is called DCf (double clustering framework). A first clustering process identifies cluster prototypes in the multidimensional data space, then the projections of these prototypes are further clustered along each dimension to provide a granulation of data. Finally, the extracted granules are described in terms of fuzzy sets that meet interpretability constraints so as to provide a qualitative description of the information granules. Different implementations of DCf are presented and compared on a medical diagnosis problem to show the utility of the proposed framework.