Clustering of longitudinal interval-valued data via mixture distribution under covariance separability

ABSTRACT

We consider the clustering of repeatedly measured ‘min-max’ type interval-valued data. We read the data as matrix variate data and assume the covariance matrix is separable for the model-based clustering (M-clustering). The use of a separable covariance matrix introduces several advantages in M-clustering, which include fewer samples required for a valid procedure. In addition, the numerical study shows that this structured matrix allows us to find the correct number of clusters more accurately compared to other commonly assumed covariance matrices. We apply the M-clustering with various covariance structures to clustering the longitudinal blood pressure data from the National Heart, Lung, and Blood Institute Growth and Health Study (NGHS).

Keywords:

• Clustering
• interval-valued data
• longitudinal data
• matrix variate data
• M-clustering
• separable covariance matrix

2010 Mathematics Subject Classifications:

• 62H30
• 62-07

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2017R1A2B2012264).