A reweighting approach to robust clustering

DOI10.1007/S11222-017-9742-XzbMath1384.62193OpenAlexW2499037020WikidataQ59396478 ScholiaQ59396478MaRDI QIDQ1702028

Francesco Dotto, Alessio Farcomeni, Agustín Mayo-Iscar, Luis Angel García-Escudero

Publication date: 27 February 2018

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: http://uvadoc.uva.es/handle/10324/18094

zbMATH Keywords

robustnesscluster analysistrimmingminimum covariance determinant estimator

Mathematics Subject Classification ID

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

Related Items (15)

Robust fitting of mixtures of GLMs by weighted likelihood ⋮ Robust variable selection for model-based learning in presence of adulteration ⋮ Robust model-based clustering with mild and gross outliers ⋮ Assessing trimming methodologies for clustering linear regression data ⋮ Robust inference for parsimonious model-based clustering ⋮ A robust approach to model-based classification based on trimming and constraints. Semi-supervised learning in presence of outliers and label noise ⋮ Model-based clustering with determinant-and-shape constraint ⋮ An impartial trimming approach for joint dimension and sample reduction ⋮ Wild adaptive trimming for robust estimation and cluster analysis ⋮ Weighted likelihood mixture modeling and model-based clustering ⋮ Robust and sparse \(k\)-means clustering for high-dimensional data ⋮ The power of monitoring: how to make the most of a contaminated multivariate sample ⋮ Comments on ``The power of monitoring: how to make the most of a contaminated multivariate sample ⋮ The power of (extended) monitoring in robust clustering. Discussion of ``The power of monitoring: how to make the most of a contaminated multivariate sample ⋮ Rejoinder to the discussion of ``The power of monitoring: how to make the most of a contaminated multivariate sample

Uses Software

• TCLUST
• otrimle

Cites Work

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