Clustering Higher Order Data: An Application to Pediatric Multi-variable Longitudinal Data

TL;DR

This paper introduces a novel model-based clustering method for high-dimensional tensor data, applied to accelerometer measurements in a pediatric health study, enabling better analysis of multivariate longitudinal data.

Contribution

It extends finite mixture models to cluster D-dimensional array data, addressing a gap in tensor data analysis for higher-order data structures.

Findings

Developed a finite mixture of multidimensional arrays model

Applied the model to pediatric accelerometer data from CHAMPION study

Demonstrated effective clustering of complex tensor data

Abstract

Physical activity levels are an important predictor of cardiovascular health and increasingly being measured by sensors, like accelerometers. Accelerometers produce rich multivariate data that can inform important clinical decisions related to individual patients and public health. The CHAMPION study, a study of youth with chronic inflammatory conditions, aims to determine the links between heart health, inflammation, physical activity, and fitness. The accelerometer data from CHAMPION is represented as 4-dimensional arrays, and a finite mixture of multidimensional arrays model is developed for clustering. The use of model-based clustering for multidimensional arrays has thus far been limited to two-dimensional arrays, i.e., matrices or order-two tensors, and the work in this paper can also be seen as an approach for clustering D-dimensional arrays for D > 2 or, in other words, for clustering order-D tensors.