The discernibility matrix is one of the most important approaches to computing positive region, reduct, core and value reduct in rough sets. The subject of this paper is to develop a parallel approach of it, called ＂...The discernibility matrix is one of the most important approaches to computing positive region, reduct, core and value reduct in rough sets. The subject of this paper is to develop a parallel approach of it, called ＂tree expression＂. Its computational complexity for positive region and reduct is O（m^2 × n） instead of O（m × n^2） in discernibility-matrix-based approach, and is not over O（n^2） for other concepts in rough sets, where rn and n are the numbers of attributes and objects respectively in a given dataset （also called an ＂information system＂ in rough sets）. This approach suits information systems with n ≥ m and containing over one million objects.展开更多
基金This work is partially supported by the National Grand Fundamental Research 973 Program of China under Grant No. 2004CB318103 and the National Nature Science Foundation of China under Grant No. 60573078.
文摘The discernibility matrix is one of the most important approaches to computing positive region, reduct, core and value reduct in rough sets. The subject of this paper is to develop a parallel approach of it, called ＂tree expression＂. Its computational complexity for positive region and reduct is O（m^2 × n） instead of O（m × n^2） in discernibility-matrix-based approach, and is not over O（n^2） for other concepts in rough sets, where rn and n are the numbers of attributes and objects respectively in a given dataset （also called an ＂information system＂ in rough sets）. This approach suits information systems with n ≥ m and containing over one million objects.
