Rule Generation Based on Reduct and Core: A Rough Set Approach
Rough set theory has evolved as one of the most important technique used for feature selection as a result of contemporary developments in data mining. One of the cardinal uses of Rough set theory is its application for rule generation. More often attribute reduction poses a major challenge for developing the theory and applications of rough set. This paper proposes a unique mathematical approach for determining the most important attribute with the help of confidence and strength of an association. The authors' approach focuses on the elimination of the redundant attributes in order to generate the effective reduct set (i.e., reduced set of necessary attributes) and formulating the core of the attribute set.