Robust Linear Clustering

DOI10.1111/j.1467-9868.2008.00682.xzbMath1231.62112OpenAlexW1994241229MaRDI QIDQ3551043

Ruben H. Zamar, R. San Martín, Stefan Van Aelst, Alfonso Gordaliza, Luis Angel García-Escudero

Publication date: 8 April 2010

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://lirias.kuleuven.be/handle/123456789/462049

zbMATH Keywords

robustnessaffine subspacestrimmingprincipal componentstrimmed \(k\)-meansorthogonal regression

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (22)

Robust fitting of mixtures of GLMs by weighted likelihood ⋮ Robust mixture regression using the \(t\)-distribution ⋮ The robust EM-type algorithms for log-concave mixtures of regression models ⋮ Nonparametric K-means algorithm with applications in economic and functional data ⋮ Improving K-means method via shrinkage estimation and LVQ algorithm ⋮ Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model ⋮ Clusterwise linear regression modeling with soft scale constraints ⋮ Finite Mixture of Linear Regression Models: An Adaptive Constrained Approach to Maximum Likelihood Estimation ⋮ A Selective Overview and Comparison of Robust Mixture Regression Estimators ⋮ Cluster analysis with cellwise trimming and applications for the robust clustering of curves ⋮ A class of fuzzy clusterwise regression models ⋮ Combining homogeneous groups of preclassified observations with application to international trade ⋮ A review of robust clustering methods ⋮ James-Stein shrinkage to improve \(k\)-means cluster analysis ⋮ Robust clusterwise linear regression through trimming ⋮ Robust clustering around regression lines with high density regions ⋮ The forward search: theory and data analysis ⋮ Robust fitting of mixture regression models ⋮ Robust fitting of hidden Markov regression models under a longitudinal setting ⋮ Mixture of linear mixed models using multivariatetdistribution ⋮ Weighted likelihood latent class linear regression ⋮ Comments on ``Data science, big data and statistics

Uses Software

• GGobi
• Silhouettes
• XGobi
• Algorithm 39

Cites Work

• Unnamed Item
• Algorithm AS 136: A K-Means Clustering Algorithm
• A maximum likelihood methodology for clusterwise linear regression
• A general trimming approach to robust cluster analysis
• GGobi: evolving from XGobi into an extensible framework for interactive data visualization
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• A fast algorithm for clusterwise linear regression
• Best approximations to random variables based on trimming procedures
• On problem solving with Hopfield neural networks
• Trimmed \(k\)-means: An attempt to robustify quantizers
• Clusters, outliers, and regression: Fixed point clusters
• Self-consistency: A fundamental concept in statistics
• Simple consistent cluster methods based on redescending M-estimators with an application to edge identification in images
• Validating visual clusters in large datasets: fixed point clusters of spectral features.
• Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering
• Two Lines Least Squares
• Finding Groups in Data
• Maximum likelihood estimates of the parameters of a mixture of two regression lines
• Asymptotic behaviour of classification maximum likelihood estimates
• Model-Based Gaussian and Non-Gaussian Clustering
• Self-Consistency and Principal Component Analysis

This page was built for publication: Robust Linear Clustering