Study On Data Reduction Based On Rough Set And Its Application In Modern Remote Education

With the developmemt of the Internet and multimedia technology, modern remote education as has been deeply impacting the traditional teaching mode as a newly teaching mode. In this new mode, various evaluating systems is one of the important component in modern remote education architecture. These evaluating systems give inspectively evaluating indexes, collect evaluating data, and then obtain decision rules by data minging. But in network enviroment, there are some problems in data procession. These are:1. Large amount of data2. Incomplete data3. The knowledge that we obtain is the truly reflection of the decision table without influence of ousider and priori knowledge. Rough set theory is the tool to solve those problems.Rough set theory proposed by Pawlak in 1982 is a mathimatic tool for handling uncertain and incomplete knowledge. It involves methods of data expressing,data learning,and data reducing. Rough set is so different from fuzzy set and other mathimatic tools that it does not need prediction of priori kownledge and is not impacted by outsider but reflect information in data objectively. So people take more and more concern on rough set in recent 20 years. Rough set becomes a powerful tool in KDD increasingly.In this paper, remote education is taken as background to research data reduction in incmplete information system. Because of incomplete data in incomplete information system, the traditional rough set models are not suitable for incomplete system. So the traditional model must be extended to satisfy the incomplete system. in this paper, rough set theory is firstly introduced, and then an improved rough set model is proposed according to the shortcoming of extended model which has been proposed.The new model is more fit for reality and has more flexible.Then a new attributes reduction alogrims are proposed based on improved model applying importance of attributes and rough entropy theory.But there is still redundant data in data table after attribute reduction. For each object, not all attribute value are necessary for last decision rule, so the reduction must be done in further step to get rid of redundant information continuously. That is calledvalue reduction. The author improves the binary discernable matrix, come up with multi-value discernable matrix and apply it to give a value reduction alogrim by constructing multi-value discernable matrix for each object to obtain dicision rules. In the last section of this paper, an application-----Teaching Evaluation System is given as application of rough set. All algorism given in this paper are applied in the application and be compared with tolerance relation.