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

Author

Listed:
  • Wookyeong Song

    (University of California, Davis)

  • Hee-Seok Oh

    (Seoul National University)

  • Ying Kuen Cheung

    (Columbia University)

  • Yaeji Lim

    (Chung-Ang University)

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.

Suggested Citation

  • Wookyeong Song & Hee-Seok Oh & Ying Kuen Cheung & Yaeji Lim, 2024. "Multi-feature clustering of step data using multivariate functional principal component analysis," Statistical Papers, Springer, vol. 65(4), pages 2109-2134, June.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:4:d:10.1007_s00362-023-01467-4
    DOI: 10.1007/s00362-023-01467-4
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    References listed on IDEAS

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