【摘要】 Rough集理论是一种新型的处理含糊和不确定性知识的数学工具 ,将Rough集理论应用于知识发现中的聚类分析 ,给出了局部不可区分关系、个体之间的局部不可区分度和总不可区分度、类之间的不可区分度、聚类结果的综合近似精度等定义 ,在此基础上提出了一种基于Rough集的层次聚类算法 ,该算法能够自动调整参数 ,以寻求更优的聚类结果 实验结果验证了该算法的可行性 ,特别是在符号属性聚类方面有较好的聚类性能更多还原

【Abstract】 Rough set theory is a new mathematical tool to deal with vagueness and uncertainty It has received considerable attention and has been applied in a variety of areas in recent years In this paper, rough set theory is applied to clustering analysis in knowledge discovery A lot of definitions such as the local indiscernibility relation, the local and total indiscernibility degree between two objects, the indiscernibility degree between two clusters and the integrated approximation rate of the clustering result are given Based on these definitions, a rough set based hierarchical clustering algorithm is proposed It can automatically adjust the parameter in order to get the more optimum result Some experiments are made on the data sets in UCI (University of California, Irvine) machine learning repository The experimental results show that the algorithm is feasible and has good clustering performance especially for symbolic attributes更多还原