Back to Search
Start Over
Multi-feature clustering of step data using multivariate functional principal component analysis.
- Source :
- Statistical Papers; Jun2024, Vol. 65 Issue 4, p2109-2134, 26p
- Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09325026
- Volume :
- 65
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Statistical Papers
- Publication Type :
- Academic Journal
- Accession number :
- 177597514
- Full Text :
- https://doi.org/10.1007/s00362-023-01467-4