A new dimension reduction method: Factor discriminant \(K\)-means

DOI10.1007/s00357-011-9085-9zbMath1226.62062OpenAlexW2010871984MaRDI QIDQ649162

Maurizio Vichi, Stefano Antonio Gattone, Roberto Rocci

Publication date: 30 November 2011

Published in: Journal of Classification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00357-011-9085-9

zbMATH Keywords

principal component analysiscluster analysis

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items

Sparse optimal discriminant clustering, Factor probabilistic distance clustering (FPDC): a new clustering method, A model-based approach to simultaneous clustering and dimensional reduction of ordinal data, An impartial trimming approach for joint dimension and sample reduction, Robust clustering around regression lines with high density regions, A mixture of generalized hyperbolic factor analyzers, Simultaneous method of orthogonal non-metric non-negative matrix factorization and constrained non-hierarchical clustering, A unified representation of simultaneous analysis methods of reduction and clustering, Reduced \(k\)-means clustering with MCA in a low-dimensional space

Uses Software

• PARVUS

Cites Work

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• Factorial and reduced \(K\)-means reconsidered
• Simultaneous component and clustering models for three-way data: Within and between approaches
• Factorial \(k\)-means analysis for two-way data.