The Nested Joint Clustering Via Dirichlet Process Mixture Model

Authors

Shengtong Han, University of Wisconsin - MilwaukeeFollow
Hongmei Zhang, University of Memphis
Wenhui Sheng, Marquette UniversityFollow
Hasan Arshad, University of Southampton

Document Type

Article

Language

eng

Publication Date

1-2019

Publisher

Taylor & Francis

Source Publication

Journal of Statistical Computation and Simulation

Source ISSN

0094-9665

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.

Comments

Accepted version. Journal of Statistical Computation and Simulation, Vol. 89, No. 5 (January 28, 2019): 815-830. DOI. © 2019 Taylor & Francis. Used with permission.

Recommended Citation

Han, Shengtong; Zhang, Hongmei; Sheng, Wenhui; and Arshad, Hasan, "The Nested Joint Clustering Via Dirichlet Process Mixture Model" (2019). Mathematical and Statistical Science Faculty Research and Publications. 38.
https://epublications.marquette.edu/math_fac/38