A quaternion clustering framework. (English) Zbl 1464.62327
Summary: Data clustering is one of the most popular methods of data mining and cluster analysis. The goal of clustering algorithms is to partition a data set into a specific number of clusters for compressing or summarizing original values. There are a variety of clustering algorithms available in the related literature. However, the research on the clustering of data parametrized by unit quaternions, which are commonly used to represent 3D rotations, is limited. In this paper we present a quaternion clustering methodology including an algorithm proposal for quaternion based \(k\)-means along with quaternion clustering quality measures provided by an enhancement of known indices and an automated procedure of optimal cluster number selection. The validity of the proposed framework has been tested in experiments performed on generated and real data, including human gait sequences recorded using a motion capture technique.
MSC:
| 62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
| 62R07 | Statistical aspects of big data and data science |
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
| 11R52 | Quaternion and other division algebras: arithmetic, zeta functions |
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