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