文献类型：会议论文

英文题名：A simple clustering knowledge presentation method on high-dimension binary data set

作者：Chen Jianbin; Sun Jie; Gao Shuli

第一作者：陈建斌

通讯作者：Chen, JB[1]

机构：[1]Beijing Union Univ, Coll Business, Dept E Business, Beijing 100025, Peoples R China

第一机构：北京联合大学商务学院|北京联合大学管理学院信息管理与电子商务系

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Business, Dept E Business, Beijing 100025, Peoples R China.|[1141721]北京联合大学商务学院;[11417]北京联合大学;[11417161]北京联合大学管理学院信息管理与电子商务系;[1141755]管理学院;

会议论文集：7th International Symposium on Test Measurement

会议日期：AUG 05-08, 2007

会议地点：Beijing, PEOPLES R CHINA

语种：英文

外文关键词：clustering; high-dimension binary data; clustering result presentation; rough set theory

摘要：The presentation and explanation of the clustering result play an important role in the technology of clustering. Based on the rough set theory on attribute space, a new clustering result presentation method is advanced. Firstly, the different properties of high-dimension binary data on object space and attribute space have been studied; secondly, the concepts of Low Approximation, Upper Approximation and Feature Precision have been defined for data set on attribute space and the Clustering Information Factor has been defined; and thirdly, a method of Clustering Knowledge Representation on Object Space and attributes space has been proposed. It is simple enough to be understood easily, can provide three kinds of information, that is the distribution of objects, the relationship of the objects distribution and the attributes set, and the rules how to assign new objects to clusters. It can provide relatively synthesis information of clustering result on object space and attribute space, reflect the clustering knowledge with rules, enable users to capture more useful pattern and to hold the internal structure of high-dimension binary data sets.