The Clustering of Categorical Data: A Comparison of a Model-based and a Distance-based Approach

DOI10.1080/03610926.2013.806665zbMath1287.62010OpenAlexW2168012671WikidataQ60471547 ScholiaQ60471547MaRDI QIDQ5419318

Christian Hennig, Laura Anderlucci

Publication date: 6 June 2014

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2013.806665

zbMATH Keywords

adjusted rand indexpartitioning around medoidsaverage silhouette widthtribal artlatent class clustering

Mathematics Subject Classification ID

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

Related Items

An empirical comparison and characterisation of nine popular clustering methods, Beyond the number of classes: separating substantive from non-substantive dependence in latent class analysis, Density-based clustering with non-continuous data, Nomclust 2.0: an R package for hierarchical clustering of objects characterized by nominal variables

Uses Software

• Latent GOLD

Cites Work

• Identifiability of parameters in latent structure models with many observed variables
• Factor analysis and AIC
• Clustering criteria for discrete data and latent class models
• Estimating the dimension of a model
• Cluster Analysis
• Finding Groups in Data
• Exploratory latent structure analysis using both identifiable and unidentifiable models