The mean shift clustering algorithm is a useful tool for clustering numeric data. Recently, Chang-Chien et al. [1] 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.
Relation:
Artificial Intelligence and Soft Computing Lecture Notes in Computer Science, 8468, 809-818
