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Robust Clustering Using Exponential Power Mixtures

Zhang, Jian, Liang, Faming (2010) Robust Clustering Using Exponential Power Mixtures. Biometrics, 66 (4). pp. 1078-1086. ISSN 0006-341X. (doi:10.1111/j.1541-0420.2010.01389.x) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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Abstract

Clustering is a widely used method in extracting useful information from gene expression data, where unknown

challenge on the conventional clustering methods, such as the Gaussian mixture (GM) model, k-means (KM), and partitioning

power mixture model to increase the robustness of clustering against general dependence and nonnormality of the data. An

unknown parameters in these mixtures. The Bayesian information criterion is then employed to determine the numbers of

that the proposed procedure outperforms GM, KM, and PAM when there are strong correlations or non-Gaussian components

in the data.

Item Type: Article
DOI/Identification number: 10.1111/j.1541-0420.2010.01389.x
Uncontrolled keywords: Expectation–conditional maximization algorithm; Exponential power mixtures; General dependence; Modelbased clustering; Sparse correlations.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jian Zhang
Date Deposited: 11 Oct 2012 16:40 UTC
Last Modified: 13 Feb 2020 04:04 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/31531 (The current URI for this page, for reference purposes)
Zhang, Jian: https://orcid.org/0000-0001-8405-2323
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