Research On The Expansion Of Rough Sets Model Based On Interval Grey Sets

The theory of Rough Sets, presented in 1982 by Polish mathematician Z. Pawlak, is a powerful mathematical tool for analyzing uncertain, fuzzy knowledge. Rough Set methods can be applied as a component of hybrid solutions in machine learning and data mining. Rough Set-based data analysis methods have been successfully applied in bioinformatics, economics and finance, medicine, multimedia, web and text mining, signal and image processing, software engineering, robotics, and engineering (e.g. power systems and control engineering). Recently, Rough Sets theory has been a hot research point in Information science.In this paper, two aspects of Rough Sets theory are studied as bellow:1) In order to get the rules in information with Rough Sets Methods, you must make attribute reduction first, and then get the rules. However, these rules are not simplest, so you have to make rules reduction.To overcome this question, a method for extracting rules based on Granule binary operation is presented in chap 3. The method, based on the relation between granule and binary numeral and the transforming relation between granule computing and binary numeral operations, obtained the rough inevitable factor and may affecting scope factor through simple binary bit-operations(i.e. AND and XOR), can extract rules quickly.2) Because of general phenomena of indefinite data or even imperfect existing, information systems that are presented to users are mostly incomplete. Simultaneously, the classical rough sets theory is based on complete information systems. Therefore, it is most significant to investigate the expansion of Rough Sets model in incomplete information system.In the chap 4, interval grey sets are introduced to represent incomplete information systems, this represented incomplete information systems are referred to Grey Information in the article. Then, K-equivalence Grey Tolerance Relation is put forward. At last, the expansions of rough sets model on grey information are studied, meanwhile, the maximal tolerance class of grey information are presented.