2008/3/24 10:00-11:00<br>
<a href="muhlenbach.html">Fabrice MUHLENBACH</a> (Associate Professor,
University of SAINT-ETIENNE, France)<br>

Multivariate supervised discretization, a neighborhood graph<br>

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.