The mean shift clustering algorithm is a useful tool for clustering numeric data. Recently, Chang-Chien et al.  proposed a mean shift clustering algorithm for circular data that are directional data on a plane. In this paper, we extend the mean shift clustering for directional data on a hypersphere.The three types of mean shift procedures are considered. With the proposed mean shift clustering for the data on a hypersphere it is not necessary to give the number of clusters since it can automatically find a final cluster number with good clustering centers. Several numerical examples are used to demonstrate its effectiveness and superiority of the proposed method.
Artificial Intelligence and Soft Computing Lecture Notes in Computer Science, 8468, 809-818