Multi-feature Clustering of Step Data using Multivariate Functional Principal Component Analysis
Abstract
This paper presents a new statistical method for clustering step data, a popular form of health record data easily obtained from wearable devices. Since step data are high-dimensional and zero-inflated, classical methods such as K-means and partitioning around medoid (PAM) cannot be applied directly. The proposed method is a novel combination of newly constructed variables that reflect the inherent features of step data, such as quantity, strength, and pattern, and a multivariate functional principal component analysis that can integrate all the features of the step data for clustering. The proposed method is implemented by applying a conventional clustering method such as K-means and PAM to the multivariate functional principal component scores obtained from these variables. Simulation studies and real data analysis demonstrate significant improvement in clustering quality.
Cite
@article{arxiv.2010.07462,
title = {Multi-feature Clustering of Step Data using Multivariate Functional Principal Component Analysis},
author = {Wookyeong Song and Hee-Seok Oh and Yaeji Lim and Ying Kuen Cheung},
journal= {arXiv preprint arXiv:2010.07462},
year = {2020}
}