The Research Of Dynamic Reduct Based On Rough Set Theory

The research of this thesis is mainly about the dynamic reduct based on rough set theory as well as how to establish and develop the decision rules after completing the reduct. This paper also covers the basic principle of rough set, the analysis of the sample strategy on different decision tables and how to make use of the discernibility matrix and discernibility function to do reduction with the decision table. Thus a relatively perfect theory and technique scheme of dynamic reduct of the decision table is formed.According to specific requirement of the reduct of different decision tables, the concept of dynamic reduct is introduced in this dissertation through analysis on current method of reduct.Using the method of dynamic reduct to deal with the decision table, is equal to take sample from the decision table in advance before the reduction of decision table, which increases the accuracy of reduction. And the relatively stable reduct are chosen from each decision subtable as the ultimate results, which also increases stability and description ability of the decision rules.At the aspect of randomly choose a sample towards different decision tables, this paper puts forward two kinds of new sampling strategy, and expatiates their application scope and concrete advantages. Combining the max rate sampling strategy and the discernibility matrix together creates the dynamic reduct algorithm.From the angle of establishing decision rules, this paper compares current methods on accessing the decision rules, and expatiates their merit and shortcoming. The paper emphasizes on two kinds of methods on directly accessing the decision rules with decision table and how to calculate decision rules with dynamic reduct. Finally, a modified algorithm of decision rules is mentioned, which perfect the error modification after the decision rules are established.In brief, the rough set theory and method is quite an efficient means to deal with complex system, which is applied to the field of dataABSTRACTmining(DM) and knowledge discovery in database(KDD). Moreover, dynamic reduct and the method on establishing the decision rules resulted from it can offer more efficient and precise data for the two fields mentioned above.