Multi-feature clustering of step data using multivariate functional principal component analysis

Wookyeong Song (), Hee-Seok Oh (), Ying Kuen Cheung () and Yaeji Lim ()
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Wookyeong Song: University of California, Davis
Hee-Seok Oh: Seoul National University
Ying Kuen Cheung: Columbia University
Yaeji Lim: Chung-Ang University

Statistical Papers, 2024, vol. 65, issue 4, No 8, 2109-2134

Abstract: Abstract This study presents a new statistical method for clustering step data, a popular form of health recording data easily obtained from wearable devices. As 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.

Keywords: Functional data; K-means; Multivariate functional principal component analysis; PAM; Step data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00362-023-01467-4

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