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The nested joint clustering via Dirichlet process mixture model

The nested joint clustering via Dirichlet process mixture model
The nested joint clustering via Dirichlet process mixture model

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.

Dirichlet mixture model, joint clustering, longitudinal data
0094-9655
815-830
Han, Shengtong
eee0c306-f700-4012-b9de-97b900034835
Zhang, Hongmei
9f774048-54d6-4321-a252-3887b2c76db0
Sheng, Wenhui
0b486f6a-72a9-48f1-bd48-cf9f2201c27b
Arshad, Hasan
917e246d-2e60-472f-8d30-94b01ef28958
Han, Shengtong
eee0c306-f700-4012-b9de-97b900034835
Zhang, Hongmei
9f774048-54d6-4321-a252-3887b2c76db0
Sheng, Wenhui
0b486f6a-72a9-48f1-bd48-cf9f2201c27b
Arshad, Hasan
917e246d-2e60-472f-8d30-94b01ef28958

Han, Shengtong, Zhang, Hongmei, Sheng, Wenhui and Arshad, Hasan (2019) The nested joint clustering via Dirichlet process mixture model. Journal of Statistical Computation and Simulation, 89 (5), 815-830. (doi:10.1080/00949655.2019.1572756).

Record type: Article

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.

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More information

Accepted/In Press date: 17 January 2019
e-pub ahead of print date: 28 January 2019
Published date: 24 March 2019
Keywords: Dirichlet mixture model, joint clustering, longitudinal data

Identifiers

Local EPrints ID: 430390
URI: http://eprints.soton.ac.uk/id/eprint/430390
ISSN: 0094-9655
PURE UUID: caad6480-c0f2-486b-803c-ccc608a80b02

Catalogue record

Date deposited: 26 Apr 2019 16:30
Last modified: 09 Nov 2021 15:02

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Contributors

Author: Shengtong Han
Author: Hongmei Zhang
Author: Wenhui Sheng
Author: Hasan Arshad

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