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

19 Jul 2019  ·  Peter A. Tait, Paul D. McNicholas, Joyce Obeid ·

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.

PDF Abstract
No code implementations yet. Submit your code now


  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here