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.

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