Improving the classification ability of DC* Algorithm

DC* (Double Clustering by A*) is an algorithm for interpretable fuzzy information granulation of data. It is mainly based on two clustering steps. The first step applies the LVQ1 algorithm to find a suitable representation of data relationships. The second clustering step is based on the A* search strategy and is aimed at finding an optimal number of fuzzy granules that can be labeled with linguistic terms. As a result, DC* is able to linguistically describe hidden relationships among available data. In this paper we propose an extension of the DC* algorithm, called DC*1.1, which improves the generalization ability of the original DC* by modifying the A* search procedure. This variation, inspired by Support Vector Machines, results empirically effective as reported in experimental results