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. © 2014 Springer International Publishing.
CITATION STYLE
Yang, M. S., Chang-Chien, S. J., & Kuo, H. C. (2014). On mean shift clustering for directional data on a hypersphere. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8468 LNAI, pp. 809–818). Springer Verlag. https://doi.org/10.1007/978-3-319-07176-3_70
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