Shao Liang-shan

Rough Set Approach for Processing Information Table

To deal with the problem of completing the information table, a new method was studied and proposed. First, define discernible vector and its addition rule by the indiscernible relation in rough set. Second, scan discernible vectors just only one time by the discernible vector addition rule in order to obtain the core attribute set and the important attributes. Then obtain a reduced attribute set by deleting redundant attributes. Finally, according to the dependence relation of condition and decision attributes, select the important breaking points,and complete the information table with the constraints of classification quality. The illustration and experiment results indicate that the method is effective and efficient.

A Novel Method of the Incomplete Web Data Recovery

In order to recover the incomplete data on the WEB page, the paper proposed a new approach based on rough set to reduce the redundant attributes, discretize the continuous attributes and fill up the incomplete data. According to indiscernible relationship, discernible vector were defined and used the discernible vector addition rule to reduce attributes. And then, depending on the concept of super-club data and entropy of the information table, discretization of the continuous attributes was implemented. Finally, by use of the corresponding relationship of condition attributes and decision attributes, the definition of interval value and interval value addition rule were defined and filled up the incomplete data. The illustration and experimental results indicate that the approach is effective and efficient.

A method for rule extraction by discernible vector

To deal with the problem of extracting rules from the information table, a new method was studied and proposed. First, define discernible vector and its addition rule by the indiscernible relation in rough set. Second, scan discernible vectors just only one time by the discernible vector addition rule in order to obtain the core attribute set and the important attributes. Then reduce attribute and attribute value by deleting redundant attributes and attribute values respectively. Finally, a concise rule set was obtained. The illustration and experiment results indicate that the method is effective and efficient for rule extraction.