Exploratory tools for clustering multivariate data

DOI10.1016/j.csda.2006.12.034zbMath1452.62028OpenAlexW2160892298MaRDI QIDQ1020815

Marco Riani, Anthony C. Atkinson

Publication date: 2 June 2009

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

Full work available at URL: https://doi.org/10.1016/j.csda.2006.12.034

zbMATH Keywords

graphicsforward search\(k\)-meansMahalanobis distancerandom startsimulation envelopes

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (17)

A robust deterministic affine-equivariant algorithm for multivariate location and scatter ⋮ A fast algorithm for robust constrained clustering ⋮ Robust methods for heteroskedastic regression ⋮ Unifying data units and models in (co-)clustering ⋮ Determining the Number of Clusters Using Multivariate Ranks ⋮ Strong consistency and robustness of the forward search estimator of multivariate location and scatter ⋮ A review of robust clustering methods ⋮ On a bounded bimodal two-sided distribution fitted to the Old-Faithful geyser data ⋮ Benchmark testing of algorithms for very robust regression: FS, LMS and LTS ⋮ The forward search: theory and data analysis ⋮ Discussion: The forward search: theory and data analysis ⋮ Italian contributions on some recent research topics in cluster analysis ⋮ Editorial: Machine learning and robust data mining ⋮ The power of monitoring: how to make the most of a contaminated multivariate sample ⋮ A parametric framework for the comparison of methods of very robust regression ⋮ Robust analysis of bibliometric data ⋮ Stable and visualizable Gaussian parsimonious clustering models

Uses Software

• mclust
• R
• MASS (R)
• Flury

Cites Work

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• Robust bivariate boxplots and multiple outlier detection.
• Exploring multivariate data with the forward search.
• Enhanced model-based clustering, density estimation, and discriminant analysis software:\newline MCLUST
• Statistical Analysis of Financial Data in S-Plus
• A Look at Some Data on the Old Faithful Geyser
• Fast Very Robust Methods for the Detection of Multiple Outliers
• Finite mixture models

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