%0 Journal Article
%T Reduction Algorithms Based on Discernibility Matrix: The Ordered Attributes Method<br>Constraint-Preserving Architecture Transformations:A Graph Rewriting Appraoch
%A WANG Jue WANG Ju Institute of Software
%A The Chinese Academy of Sciences
%A Beijing
%A PR China E-mail: wangj @iamail ia ar cn Received June
%A revised February
%A <br>王珏
%A 王驹
%J 计算机科学技术学报
%D 2001
%I 
%X In this paper, we present reduction algorithms based on the principle of Skowron's discernibility matrix - the ordered attributes method. The completeness of the algorithms for Pawlak reduct and the uniqueness for a given order of the attributes are proved. Since a discernibility matrix requires the size of the memory of U2, U is a universe of objects, it would be impossible to apply these algorithms directly to a massive object set. In order to solve the problem, a so-called quasi-discernibility matrix and two reduction algorithms are proposed. Although the proposed algorithms are incomplete for Pawlak reduct, their opimal paradigms ensure the completeness as long as they satisfy some conditions. Finally we consider the problem on the reduction of distributive object sets.
%K rough set theory
%K principle of discernibility matrix
%K inductive machine learning<br>
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=F57FEF5FAEE544283F43708D560ABF1B&aid=F4D53188DBB0C220760D2945C88DB773&yid=14E7EF987E4155E6&vid=7801E6FC5AE9020C&iid=B31275AF3241DB2D&sid=2B25C5E62F83A049&eid=2B25C5E62F83A049&journal_id=1000-9000&journal_name=计算机科学技术学报&referenced_num=38&reference_num=0