Reduced \(k\)-means clustering with MCA in a low-dimensional space

DOI10.1007/s00180-014-0544-8zbMath1317.65042OpenAlexW2060858701MaRDI QIDQ2354741

Hiroshi Yadoshita, Masaki Mitsuhiro

Publication date: 24 July 2015

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-014-0544-8

zbMATH Keywords

ALScategorical datasimultaneous analysis

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 (2)

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

Cites Work

• Unnamed Item
• Unnamed Item
• A new dimension reduction method: Factor discriminant \(K\)-means
• Proceedings of Reisensburg 2010
• An extension of multiple correspondence analysis for identifying heterogeneous subgroups of respondents
• Factorial and reduced \(K\)-means reconsidered
• Fuzzy Cluster Multiple Correspondence Analysis
• Factorial \(k\)-means analysis for two-way data.

This page was built for publication: Reduced \(k\)-means clustering with MCA in a low-dimensional space