Linear grouping using orthogonal regression

DOI10.1016/j.csda.2004.11.011zbMath1431.62273OpenAlexW2108832093MaRDI QIDQ959227

Xiao-Gang Wang, Rong Zhu, Ruben H. Zamar, Stefan Van Aelst

Publication date: 11 December 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10315/923

zbMATH Keywords

orthogonal regressionlinear grouping

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Pattern recognition, speech recognition (68T10)

Related Items (16)

Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model ⋮ Sensor fusion for SLAM based on information theory ⋮ Cluster analysis with cellwise trimming and applications for the robust clustering of curves ⋮ A class of fuzzy clusterwise regression models ⋮ Graphical and Computational Tools to Guide Parameter Choice for the Cluster Weighted Robust Model ⋮ A review of robust clustering methods ⋮ Robust clusterwise linear regression through trimming ⋮ A general trimming approach to robust cluster analysis ⋮ Multiscale co-clustering for tensor data based on canonical polyadic decomposition and slice-wise factorization ⋮ A robust linear grouping algorithm ⋮ Finding approximate solutions to combinatorial problems with very large data sets using BIRCH ⋮ Exploring the number of groups in robust model-based clustering ⋮ Robust clustering around regression lines with high density regions ⋮ Fuzzy clusterwise linear regression analysis with symmetrical fuzzy output variable ⋮ Model-based linear clustering ⋮ Comments on ``Data science, big data and statistics

Uses Software

• MINITAB
• AS 136
• Algorithm 39

Cites Work

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• Estimating the Number of Clusters in a Data Set Via the Gap Statistic
• Algorithm AS 136: A K-Means Clustering Algorithm
• A maximum likelihood methodology for clusterwise linear regression
• Design of hybrids for the minimum sum-of-squares clustering problem
• Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator
• Experiments with, and on, algorithms for maximum likelihood clustering
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• A fast algorithm for clusterwise linear regression
• A Survey of Recent Advances in Hierarchical Clustering Algorithms
• Robust estimation in the errors-in-variables model
• Radial estimates and the test for sphericity
• Finding Groups in Data
• Model-Based Gaussian and Non-Gaussian Clustering

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