An efficient classifier design integrating rough set and set oriented database operations

Feature subset selection and dimensionality reduction of data are fundamental and most explored area of research in machine learning and data mining domains. Rough set theory (RST) constitutes a sound basis for data mining, can be used at different phases of knowledge discovery process. In the paper, by integrating the concept of RST and relational algebra operations, a new attribute reduction algorithm has been presented to select the minimum set of attributes, called reducts, required for classification of data. Firstly, the conditional attributes are partitioned into different groups according to their score, calculated using projection ( ) and division (÷) operations of relational algebra. The groups based on their scores