Multiclus: a new method for simultaneously performing multidimensional scaling and cluster analysis

DOI10.1007/BF02294590zbMath0727.62107OpenAlexW1990211480MaRDI QIDQ803704

Kamel Jedidi, Daniel J. Howard, Wayne S. Desarbo

Publication date: 1991

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02294590

zbMATH Keywords

maximum likelihood estimationcluster analysisMonte Carlo analysismultidimensional scalingconsumer psychologyE-M algorithmmixtures of multivariate conditional normal distributionsMULTICLUS procedure

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Clustering in the social and behavioral sciences (91C20) Applications of statistics to psychology (62P15) One- and multidimensional scaling in the social and behavioral sciences (91C15)

Related Items (12)

Cluster differences unfolding for two-way two-mode preference rating data ⋮ A Bayesian vector multidimensional scaling procedure incorporating dimension reparameterization with variable selection ⋮ A three-way clusterwise multidimensional unfolding procedure for the spatial representation of context dependent preferences ⋮ An extension of multiple correspondence analysis for identifying heterogeneous subgroups of respondents ⋮ Factorial \(k\)-means analysis for two-way data. ⋮ Random effects diagonal metric multidimensional scaling models ⋮ Modeling viewpoint shifts in probabilistic choice ⋮ A latent class multidimensional scaling model for two-way one-mode continuous rating dissimilarity data ⋮ A random effects individual difference multidimensional scaling model. ⋮ Segmentation-based competitive analysis with MULTICLUS and topology representing networks ⋮ A Bayesian multidimensional scaling procedure for the spatial analysis of revealed choice data ⋮ A latent class procedure for the structural analysis of two-way compositional data

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