Snipping for robust \(k\)-means clustering under component-wise contamination

DOI10.1007/s11222-013-9410-8zbMath1332.62203OpenAlexW2032126425WikidataQ59396923 ScholiaQ59396923MaRDI QIDQ260978

Alessio Farcomeni

Publication date: 22 March 2016

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

Full work available at URL: https://doi.org/10.1007/s11222-013-9410-8

zbMATH Keywords

outliersrobustnessclusteringtrimming\(k\)-meanssnipping

Mathematics Subject Classification ID

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

Related Items (14)

Robust regression estimation and inference in the presence of cellwise and casewise contamination ⋮ Robust regression with compositional covariates including cellwise outliers ⋮ A fuzzy approach to robust regression clustering ⋮ Cluster analysis with cellwise trimming and applications for the robust clustering of curves ⋮ Robust inference for parsimonious model-based clustering ⋮ Multiple scaled contaminated normal distribution and its application in clustering ⋮ Wild adaptive trimming for robust estimation and cluster analysis ⋮ Comments on: ``Multivariate functional outlier detection ⋮ Robust regression estimation and variable selection when cellwise and casewise outliers are present ⋮ S-estimation of hidden Markov models ⋮ Comments on: ``Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination ⋮ Robust and sparse \(k\)-means clustering for high-dimensional data ⋮ The power of (extended) monitoring in robust clustering. Discussion of ``The power of monitoring: how to make the most of a contaminated multivariate sample ⋮ Discussion of: ``The power of monitoring: how to make the most of a contaminated multivariate sample

Uses Software

• UCI-ml

Cites Work

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