Discovering Maximal Generalized Decision Rules in Databases

The volume of data being generated nowadays is increasing at phenomenal rate. Extracting useful knowledge from such data collections is an important and challenging issue. A promising technique is the rough set approach, a new mathematical method to data analysis based on classification of objects into similarity classes, which are indiscernible with respect to some features. This paper focuses on discovering maximal generalized decision rules in databases based on a simple or multiple regression, generalized theory, and decision matrix.