اكتشاف قواعد القرار المحددة و غير المحددة من قواعد المعطيات

Abstract EN

Knowledge Discovery in Databases (KDD) is an active research area with the promise for a high profit in many business and scientific applications.

The grand challenge of knowledge discovery in databases is to automatically process large quantities of raw data, identify the most significant and meaningful patterns, and present this knowledge in an appropriate form for achieving the user’s goal.

Knowledge discovery systems face challenging problems from the real-world databases which tend to be very large, redundant, noisy, and dynamic.

Each of these problems has been addressed to some extent within the machine learning.

Handling some of these problems while producing useful knowledge efficiently and effectively is the main focus of this paper.

In this paper, for handling some of these problems, the main objective in our study is to exploit a theory of rough sets in discovery of deterministic and nondeterministic decision rules from databases