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 t...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.展开更多
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.60474041)湖南省自然科学基金(the Natural Science Foundation of Hunan Province of China under Grant No.06JJ20075)。
文摘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.
