Hamming Clustering: a new approach to rule extraction

A new algorithm, called Hamming Clustering (HC), is proposed to extract a set of rules underlying a given classification problem. It is able to reconstruct the and-or expression associated with any Boolean function from a training set of samples. The basic kernel of the method is the generation of clusters of input patterns that belong to the same class and are close each other according to the Hamming distance. Inputs are identified, which do not influence the final output, thus automatically reducing the complexity of the final set of rules. Its application to artificial and real-world benchmarks has allowed a first evaluation of the performances exhibited by HC. In particular, in the diagnosis of breast cancer HC yielded a reduced set of rules solving the associated classification problem.