The Research Of Data Mining Method Based On Rough Set Theory

With the development of computer technology, people are confronted with moreand more data in Information Age. How to find the internal information in data is ahot point with which people are concerned. As traditional DBMS can't afford therequirement which people want to extract hidden information, the conceptions ofKDD and Data Ming are expounded. Knowledge discovery in databases is thenontrivial process of identifying valid, novel, potentially useful and ultimatelyunderstandable patterns in databases. Data Mining is the core step during the courseof Knowledge Discovery in database. At present, it is a quite active research field.The theory of Rough Sets, presented in 1982 by Polish mathematician Pawlak Z,is a powerful mathematical tool for analyzing uncertain, fuzzy knowledge. Rough sets,as a new hotspot in the field of artifical intelligence, can effectively deal with theexpression and deduction of incomplete, uncertain knowledge. The theory of RoughSets is specially fit for the application to Data-Mining because of it features. Now themethod of Data Mining based on Rough Sets has become one of the main methods ofData Mining. The study on Data Mining based Rough Sets has greatly theoretical andrealistic meaning.This thesis researches the method of Data Mining based on Rough Setssystematically and deeply. The main contents are listed as follows:1.The correclative theory of Rough Sets and Data Mining was delivered in thisdissertation. The thesis summarizes and discusses their developmental trends and hotresearch fields. All of the above become the basis for this thesis.2.The paper researches the reduction algorithm deeply, which consists ofattribute reduction and attribute value reduction. Attribute reduction algorithm is thekey for the model of data mining based on the Rough Sets.3.On the basis of known reduction algorithms, an improved attribute reductionalgorithm is presented in this paper. This heuristic, improved attribute reductionalgorithm, based on the HORAFA algorithm, can guarantee a reduction of theinformation system.4.An improved model of data mining based on the Rough Sets is presented afterlucubrating the deficiencies of the theory of traditional Rough Sets. The modelconsists of previous management module, attribute reduction module and rulesgeneration module. An instance is given to prove the feasibility of the model.The drawback of this paper is that it only uses classical Rough Set conception inmaking the Data Mining model. The application range is not wide without using otherextended model. Data Mining is in a booming stage and there are many problemsworth studying on the application of Rough Set Theory in this field. Our work is just abeginning, and related work need to be further developed.