Abstract
This paper centers on the problem of extracting
intensional information for a set of objects from an
object-oriented database. In our approach, the
extracted intensional information for the given set of
objects are described by object- oriented queries that
compute this set of objects. The paper discusses the
architecture of a knowledge discovery system, called
MASSON, which employs genetic programming to find such
queries, moreover, we will show how interesting queries
that describe commonalities within a set of objects are
automatically generated, modified, evaluated, and
selected; we will also discuss how the search for the
"best" query is conducted by the MASSON system. We
also report on an experiment that evaluated the
knowledge discovery capability of MASSON.
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