Multivariate supervised discretization,
a neighborhood graph approach
Fabrice Muhlenbach and Ricco Rakotomalala
Lumière University -- Lyon II
5 avenue Pierre Mendès-France
F-69676 BRON Cedex -- FRANCE
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.