Multivariate supervised discretization, a neighborhood graph approach

We present a new discretization method in the context of supervised learning. This method entitled HyperCluster Finder is characterized by its supervised and polythetic behavior. The method is based on the notion of clusters and processes in two steps. First, a neighborhood graph construction from the learning database allows discovering homogenous clusters. Second, the minimal and maximal values of each cluster are transferred to each dimension in order to define some boundaries to cut the continuous attribute in a set of intervals. The discretization abilities of this method are illustrated by some examples, in particular processing the XOR problem.