scholarly journals Penalized model-based clustering with unconstrained covariance matrices

2009 ◽  
Vol 3 (0) ◽  
pp. 1473-1496 ◽  
Author(s):  
Hui Zhou ◽  
Wei Pan ◽  
Xiaotong Shen
Keyword(s):  
Covariance Matrices ◽  
Model Based Clustering ◽  
Model Based

scholarly journals Model-based clustering with sparse covariance matrices

2018 ◽  
Vol 29 (4) ◽  
pp. 791-819 ◽  
Author(s):  
Michael Fop ◽  
Thomas Brendan Murphy ◽  
Luca Scrucca
Keyword(s):  
Covariance Matrices ◽  
Model Based Clustering ◽  
Model Based

Robust Clustering Method in the Presence of Scattered Observations

2016 ◽  
Vol 28 (6) ◽  
pp. 1141-1162
Author(s):  
Akifumi Notsu ◽  
Shinto Eguchi
Keyword(s):  
Data Analysis ◽  
Degrees Of Freedom ◽  
Covariance Matrices ◽  
The Other ◽  
Proper Solution ◽  
Clustering Method ◽  
Robust Clustering ◽  
Standard Methods ◽  
Model Based Clustering ◽  
Model Based

Contamination of scattered observations, which are either featureless or unlike the other observations, frequently degrades the performance of standard methods such as K-means and model-based clustering. In this letter, we propose a robust clustering method in the presence of scattered observations called Gamma-clust. Gamma-clust is based on a robust estimation for cluster centers using gamma-divergence. It provides a proper solution for clustering in which the distributions for clustered data are nonnormal, such as t-distributions with different variance-covariance matrices and degrees of freedom. As demonstrated in a simulation study and data analysis, Gamma-clust is more flexible and provides superior results compared to the robustified K-means and model-based clustering.

scholarly journals Model-Based Clustering with Measurement or Estimation Errors

Genes ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 185 ◽  
Author(s):  
Wanli Zhang ◽  
Yanming Di
Keyword(s):  
Finite Mixture Models ◽  
Estimation Error ◽  
Covariance Matrices ◽  
Finite Mixture ◽  
Estimation Errors ◽  
Data Set ◽  
Component Distribution ◽  
Model Based Clustering ◽  
Model Based ◽  
Error Covariance

Model-based clustering with finite mixture models has become a widely used clustering method. One of the recent implementations is MCLUST. When objects to be clustered are summary statistics, such as regression coefficient estimates, they are naturally associated with estimation errors, whose covariance matrices can often be calculated exactly or approximated using asymptotic theory. This article proposes an extension to Gaussian finite mixture modeling—called MCLUST-ME—that properly accounts for the estimation errors. More specifically, we assume that the distribution of each observation consists of an underlying true component distribution and an independent measurement error distribution. Under this assumption, each unique value of estimation error covariance corresponds to its own classification boundary, which consequently results in a different grouping from MCLUST. Through simulation and application to an RNA-Seq data set, we discovered that under certain circumstances, explicitly, modeling estimation errors, improves clustering performance or provides new insights into the data, compared with when errors are simply ignored, whereas the degree of improvement depends on factors such as the distribution of error covariance matrices.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification

Model-Based Clustering

2020 ◽  
pp. 509-529
Author(s):  
G.J. McLachlan ◽  
S.I. Rathnayake ◽  
S.X. Lee
Keyword(s):  
Model Based Clustering ◽  
Model Based

scholarly journals Automated gating of flow cytometry data via robust model-based clustering

2008 ◽  
Vol 73A (4) ◽  
pp. 321-332 ◽  
Author(s):  
Kenneth Lo ◽  
Ryan Remy Brinkman ◽  
Raphael Gottardo
Keyword(s):  
Flow Cytometry ◽  
Model Based Clustering ◽  
Flow Cytometry Data ◽  
Model Based ◽  
Robust Model

Model-based principal components of covariance matrices

2010 ◽  
Vol 63 (1) ◽  
pp. 113-137 ◽  
Author(s):  
Robert J. Boik ◽  
Kamolchanok Panishkan ◽  
Scott K. Hyde
Keyword(s):  
Principal Components ◽  
Covariance Matrices ◽  
Model Based
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