Document Detail


Robust clustering using exponential power mixtures.
MedLine Citation:
PMID:  20163406     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Summary Clustering is a widely used method in extracting useful information from gene expression data, where unknown correlation structures in genes are believed to persist even after normalization. Such correlation structures pose a great challenge on the conventional clustering methods, such as the Gaussian mixture (GM) model, k-means (KM), and partitioning around medoids (PAM), which are not robust against general dependence within data. Here we use the exponential power mixture model to increase the robustness of clustering against general dependence and nonnormality of the data. An expectation-conditional maximization algorithm is developed to calculate the maximum likelihood estimators (MLEs) of the unknown parameters in these mixtures. The Bayesian information criterion is then employed to determine the numbers of components of the mixture. The MLEs are shown to be consistent under sparse dependence. Our numerical results indicate that the proposed procedure outperforms GM, KM, and PAM when there are strong correlations or non-Gaussian components in the data.
Authors:
Jian Zhang; Faming Liang
Related Documents :
16161806 - Applications of binary segmentation to the estimation of quantal response curves and sp...
18252366 - Clustering of symbolic objects using gravitational approach.
16772566 - Spatial and temporal patterns of herd somatic cell score in france.
16177696 - Space-time clusters with flexible shapes.
25360066 - Structured learning of gaussian graphical models.
18218526 - Time-harmonic impedance tomography using the t-matrix method.
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Biometrics     Volume:  66     ISSN:  1541-0420     ISO Abbreviation:  Biometrics     Publication Date:  2010 Dec 
Date Detail:
Created Date:  2010-12-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1078-86     Citation Subset:  IM    
Copyright Information:
© 2010, The International Biometric Society.
Affiliation:
Department of Mathematics, University of York, Heslington, York, YO10 5DD, U.K. Department of Statistics, Texas A&M University, College Station, Texas 77843-3143, U.S.A.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Continuous covariates in mark-recapture-recovery analysis: a comparison of methods.
Next Document:  Dilating the vagina to prevent damage from radiotherapy: systematic review of the literature.