A new biplot procedure with joint classification of objects and variables by fuzzy \(c\)-means clustering

DOI10.1007/s11634-014-0184-4zbMath1414.62022OpenAlexW2016629157MaRDI QIDQ2418397

Shin-ichi Mayekawa, Naoto Yamashita

Publication date: 3 June 2019

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-014-0184-4

zbMATH Keywords

singular value decompositionprincipal component analysisbiplotalternating least squaresfuzzy \(c\)-means clustering

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Eigenvalues, singular values, and eigenvectors (15A18) Fuzzy analysis in statistics (62A86)

Related Items (4)

Logistic biplot for nominal data ⋮ Fuzzy data analysis and classification. Special issue in memoriam of Professor Lotfi A. Zadeh, father of fuzzy logic ⋮ Principal component analysis constrained by layered simple structures ⋮ A modified \(k\)-means clustering procedure for obtaining a cardinality-constrained centroid matrix

Uses Software

• UCI-ml
• Algorithm 39

Cites Work

• Regularized fuzzy clusterwise ridge regression
• A class of fuzzy clusterwise regression models
• A generalization of Kristof's theorem on the trace of certain matrix products
• The fuzzy approach to statistical analysis
• Fuzzy clustering algorithms and their cluster validity
• Clusterwise linear regression
• Three-mode component analysis with crisp or fuzzy partition of units
• Constrained principal component analysis of standardized data for biplots with unit-length variable vectors
• Simultaneous component and clustering models for three-way data: Within and between approaches
• Fuzzy clusterwise generalized structured component analysis
• Fuzzy Cluster Multiple Correspondence Analysis
• Modern Multivariate Statistical Techniques
• Detection and Characterization of Cluster Substructure I. Linear Structure: Fuzzy c-Lines
• Fuzzy sets
• The biplot graphic display of matrices with application to principal component analysis
• Data Clustering: Theory, Algorithms, and Applications
• Factorial \(k\)-means analysis for two-way data.
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