CURE algorithm is an efficient hierarchical clustering algorithm for large data sets. This paper presents an improved CURE algorithm, named ISE-RS-CURE. The algorithm adopts a sample extraction algorithm combined with statistical ideas, which can reasonably select sample points according to different data densities and can improve the representation of sample sets. When the sample set is extracted, the data set is divided at the same time, which can help to reduce the time consumption in the non-sample set allocation process. A selection strategy based on partition influence factor is proposed for the selection of representative points, which comprehensively considers the overall correlation between the data in the region where a representative point is located, so as to improve the rationality of the representative points. Experiments show that the improved CURE algorithm proposed in this paper can ensure the accuracy of the clustering results and can also improve the operating efficiency.