Interpretable Granulation of Medical Data with DC
Corrado Mencar, Arianna Consiglio, Anna Maria Fanelli · 2007
In this paper we describe an approach for mining interpretable diagnostic rules through a fuzzy information granulation process. Specifically, this process is performed by the DC* algorithm (Double Clustering with A*), which is aimed at mining from data a set of fuzzy information granules that satisfy a number of interpretability constraints. Such granules can be labelled with linguistic terms and used as building blocks for deriving diagnostic rules. The DC* is based on two clustering steps. The first step applies the LVQ1 algorithm to find a number of prototypes in the input space, which represent hidden relationships among data. The second clustering step .based on the A* search. takes place on the projections of such prototypes, and is aimed at finding an optimal number of granules that verify interpretability constraints. The application of DC* to two well-known medical datasets provided a set of intelligible rules with satisfactory accuracy.