The Research On Data Mining Algorithms Based-on Rough Set Theory

People need to manage with more and more data with the improvement of database technology and the widely use of database management system. They want to find out the relationships and rules concealed in data, to use those data efficiently and morenover, to forecast the improvemental trend of the existing data by advanced analysis. Thus, data mining was proposed. Data mining is novel and active and is one of the most forward lines of database and information decision research. The rough set theory (RST), which was introduced by Pawlak Z. in 1982, is a useful tool to deal with vagueness and uncertainty. With rough set theory, decision or classification rules can be deduced during the process of knowledge reduction, with classify ability not decreased. RST is more objective in describing and dealing with vagueness and uncertainty than some other methods for it does not need any preliminary information of data.This paper firstly introduces the basic concepts and improvements and applications of data mining and rough set theory, and the rough set based data mining methods; then, after draw comparisons of rough sets and neural networks, it presents a novel method to construction combined classifier, by introducing reduction and reasoning method and a basic notion of RST namely attribute importance into the constructing process, this result in a more intelligible combined classifier with accuracy and performance improved. Thirdly, it takes study on some existing reduction algorithms and makes an intensive study of parallel reduction algorithm. Finally, it makes a conclusion of this paper and the outlook of future research.