摘要
Rough Set Theory, which has been found applicable and useful in many fields, is now a very effective method in data mining research. However, when the decision table is an incomplete one, with the original rough set theory proposed by Z. Pawlak, one can't get satisfactory results. In this paper an approach based on limited valued tolerance relation and majority inclusion relation is proposed. And furthermore a new attribute reduction method called extended discernable matrix is given. As this model is somewhat a combination of fuzzy means and majority inclusion relation, it is more effective than the previous models in practice.
Rough Set Theory, which has been found applicable and useful in many fields, is now a very effective method in data mining research. However, when the decision table is an incomplete one, with the original rough set theory proposed by Z. Pawlak, one can't get satisfactory results. In this paper an approach based on limited valued tolerance relation and majority inclusion relation is proposed. And furthermore a new attribute reduction method called extended discernable matrix is given. As this model is somewhat a combination of fuzzy means and majority inclusion relation, it is more effective than the previous models in practice.
出处
《计算机科学》
CSCD
北大核心
2003年第4期153-155,共3页
Computer Science
基金
国家自然科学基金(No.60275019)
国家863项目(No.2001AA115460)
山西省自然科学基金
