2008/3/24 10:00-11:00
Fabrice MUHLENBACH (Associate Professor, University of SAINT-ETIENNE, France)
Multivariate supervised discretization, a neighborhood graph
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 homogeneous 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 several examples.