Factorial and reduced \(K\)-means reconsidered

DOI10.1016/j.csda.2010.02.009zbMath1284.62396OpenAlexW2085919530MaRDI QIDQ2445639

Eva Ceulemans, Marieke E. Timmerman, Maurizio Vichi, Henk A. L. Kiers

Publication date: 14 April 2014

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

Full work available at URL: https://doi.org/10.1016/j.csda.2010.02.009

zbMATH Keywords

dimensional reductioncluster analysis\(K\)-meansfactorial model

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items

Functional factorial \(K\)-means analysis ⋮ KSC-N: clustering of hierarchical time profile data ⋮ Recipes for sparse LDA of horizontal data ⋮ Fixed factor analysis with clustered factor score constraint ⋮ Strong Consistency of ReducedK-means Clustering ⋮ Dimension-reduced clustering of functional data via subspace separation ⋮ Clustering of multivariate binary data with dimension reduction via \(L_{1}\)-regularized likelihood maximization ⋮ Factor probabilistic distance clustering (FPDC): a new clustering method ⋮ A new dimension reduction method: Factor discriminant \(K\)-means ⋮ An impartial trimming approach for joint dimension and sample reduction ⋮ Modeling differences in the dimensionality of multiblock data by means of clusterwise simultaneous component analysis ⋮ Using projection-based clustering to find distance- and density-based clusters in high-dimensional data ⋮ A survey on soft subspace clustering ⋮ Clustering of functional data in a low-dimensional subspace ⋮ 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 ⋮ Strong consistency of factorial \(k\)-means clustering ⋮ Reduced \(k\)-means clustering with MCA in a low-dimensional space

Uses Software

• clusfind

Cites Work

• OCLUS: an analytic method for generating clusters with known overlap
• Selecting among multi-mode partitioning models of different complexities: a comparison of four model selection criteria
• The varimax criterion for analytic rotation in factor analysis
• Selection of variables in cluster analysis: An empirical comparison of eight procedures
• Finding Groups in Data
• Factorial \(k\)-means analysis for two-way data.
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