• A NOVEL APPROACH TO ATTRIBUTE REDUCTION UNDER DATA MINING PROCESS USING ROUGH SET THEORY

Abstract

Data mining is the process of selecting, exploring and modeling large amounts of data to uncover the previously unknown patterns. Data mining has gradually become an important and active area of research because of theoretical challenges and real-world applications associated with the problem of extracting interesting unknown patterns from large repositories. Attribute reduction has become an important pre-processing task to reduce the complexity of the data mining task. In rough set theory, accuracy and roughness are used to characterize uncertainty of a set and approximation accuracy is employed to depict accuracy of a rough classification. In this paper, we describe a novel approach for attribute reduction to Data mining process using rough set theory. Which is based on attribute reducts on the granular view of the information system.