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Paper details
Number 1 - March 2020
Volume 30 - 2020
A quaternion clustering framework
Michał Piórek, Bartosz Jabłoński
Abstract
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.
Keywords
data clustering, quaternions data processing, human gait data processing