A method for preprocessing an incomplete information table

This paper studied the problems of filling up incomplete data, reducing redundant attributes and discretizing continuous attributes in preprocessing the incomplete information table with continuous attributes in a rough set. According to the concept of interval value and the consistency of condition attributes and decision attributes, a plus rule for interval values was defined to filling up the incomplete data. Depending on the conception of classification, the discernible vector was defined and the discernible vector addition rule was used to delete redundant attributes. By use of the super-club data and entropy of the information table, the discretization of continuous attributes was implemented. The illustration and experimental results indicate that the method is effective.