Multivariate methods using mixtures: correspondence analysis, scaling and pattern-detection

DOI10.1016/j.csda.2013.05.013zbMath1471.62162OpenAlexW2081534407MaRDI QIDQ1621303

Shirley Pledger, Richard A. Arnold

Publication date: 8 November 2018

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

Full work available at URL: https://doi.org/10.1016/j.csda.2013.05.013

zbMATH Keywords

dimension reductioncluster analysisfuzzy clusteringcorrespondence analysismultidimensional scalingbiclusteringfinite mixturebiplotsassociation analysisdata visualisation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (13)

Editorial: The 2nd special issue on advances in mixture models ⋮ Biclustering models for two-mode ordinal data ⋮ Categorising count data into ordinal responses with application to ecological communities ⋮ Model-based simultaneous clustering and ordination of multivariate abundance data in ecology ⋮ Mixture-based clustering for the ordered stereotype model ⋮ Model-based co-clustering for ordinal data ⋮ Matching and clustering in square contingency tables. Who matches with whom in the Spanish labour market ⋮ Finite mixture biclustering of discrete type multivariate data ⋮ On mathematical optimization for clustering categories in contingency tables ⋮ Parameter-wise co-clustering for high-dimensional data ⋮ Biclustering multivariate discrete longitudinal data ⋮ Model-based approach for household clustering with mixed scale variables ⋮ Bayesian model-based clustering for longitudinal ordinal data

Uses Software

• R
• TWINSPAN

Cites Work

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