The nested joint clustering via Dirichlet process mixture model

ABSTRACT

This article focuses on the clustering problem based on Dirichlet process (DP) mixtures. To model both time invariant and temporal patterns, different from other existing clustering methods, the proposed semi-parametric model is flexible in that both the common and unique patterns are taken into account simultaneously. Furthermore, by jointly clustering subjects and the associated variables, the intrinsic complex shared patterns among subjects and among variables are expected to be captured. The number of clusters and cluster assignments are directly inferred with the use of DP. Simulation studies illustrate the effectiveness of the proposed method. An application to wheal size data is discussed with an aim of identifying novel temporal patterns among allergens within subject clusters.

KEYWORDS:

• Dirichlet mixture model
• joint clustering
• longitudinal data

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research work is partially supported by National Institutes of Health research fund, R21 AI099367, Hongmei Zhang (PI).