Abstract: The school / university orientation interests a broad and
often badly informed public. Technically, it is an important
multicriterion decision problem, which supposes the combination of
much academic professional and/or lawful knowledge, which in turn
justifies software resorting to the techniques of Artificial Intelligence.
CORUS is an expert system of the "Conseil et ORientation
Universitaire et Scolaire", based on a knowledge representation
language (KRL) with rules and objects, called/ known as Ibn Rochd.
CORUS was developed thanks to DéGSE, a workshop of cognitive
engineering which supports this LRC. CORUS works out many
acceptable solutions for the case considered, and retains the most
satisfactory among them. Several versions of CORUS have extended
its services gradually.
Abstract: Knowledge Discovery in Databases (KDD) has
evolved into an important and active area of research because of
theoretical challenges and practical applications associated with the
problem of discovering (or extracting) interesting and previously
unknown knowledge from very large real-world databases. Rough
Set Theory (RST) is a mathematical formalism for representing
uncertainty that can be considered an extension of the classical set
theory. It has been used in many different research areas, including
those related to inductive machine learning and reduction of
knowledge in knowledge-based systems. One important concept
related to RST is that of a rough relation. In this paper we presented
the current status of research on applying rough set theory to KDD,
which will be helpful for handle the characteristics of real-world
databases. The main aim is to show how rough set and rough set
analysis can be effectively used to extract knowledge from large
databases.