Improving the Classification Ability of DC~* Algorithm

摘要

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.