On the breakdown behavior of the TCLUST clustering procedure

DOI10.1007/s11749-012-0312-4zbMath1273.62146OpenAlexW2041432803MaRDI QIDQ364182

C. Ruwet, Agustín Mayo-Iscar, Alfonso Gordaliza, Luis Angel García-Escudero

Publication date: 5 September 2013

Published in: Test (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11749-012-0312-4

zbMATH Keywords

robustnessbreakdown point

Mathematics Subject Classification ID

Nonparametric robustness (62G35) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (5)

Robust model-based clustering with mild and gross outliers ⋮ Robust inference for parsimonious model-based clustering ⋮ Wild adaptive trimming for robust estimation and cluster analysis ⋮ Robust, fuzzy, and parsimonious clustering, based on mixtures of factor analyzers ⋮ Parsimonious mixtures of multivariate contaminated normal distributions

Uses Software

• clusfind

Cites Work

• Exploring the number of groups in robust model-based clustering
• Robust fitting of mixtures using the trimmed likelihood estimator
• The influence function of the TCLUST robust clustering procedure
• Dissolution point and isolation robustness: Robustness criteria for general cluster analysis methods
• A general trimming approach to robust cluster analysis
• Trimmed ML estimation of contaminated mixtures
• A constrained formulation of maximum-likelihood estimation for normal mixture distributions
• Trimmed \(k\)-means: An attempt to robustify quantizers
• A robust method for cluster analysis
• Trimming algorithms for clustering contaminated grouped data and their robustness
• A review of robust clustering methods
• Finding Groups in Data
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Finite mixture models
• Robustness Properties of k Means and Trimmed k Means
• Comprehensive Definitions of Breakdown Points for Independent and Dependent Observations
• 10.1162/1532443041827943
• Unnamed Item

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