Model-based clustering of multivariate ordinal data relying on a stochastic binary search algorithm

DOI10.1007/s11222-015-9585-2zbMath1505.62068OpenAlexW583546247MaRDI QIDQ132947

Julien Jacques, Christophe Biernacki, Christophe Biernacki, Julien Jacques

Publication date: 14 June 2015

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://hal.inria.fr/hal-01052447v2/file/Biernacki-Jacques-OrdinalDataModel.pdf

zbMATH Keywords

latent variablesordinal dataAECM algorithmbinary search algorithm

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (13)

Co-Clustering of Ordinal Data via Latent Continuous Random Variables and Not Missing at Random Entries ⋮ A comprehensive framework of regression models for ordinal data ⋮ Model-based co-clustering for ordinal data ⋮ Ordinal trees and random forests: score-free recursive partitioning and improved ensembles ⋮ Unifying data units and models in (co-)clustering ⋮ Cumulative and CUB Models for Rating Data: A Comparative Analysis ⋮ A survey on model-based co-clustering: high dimension and estimation challenges ⋮ Archetypal analysis for ordinal data ⋮ Model-based co-clustering for mixed type data ⋮ Rejoinder to the discussion of ``The class of CUB models: statistical foundations, inferential issues and empirical evidence ⋮ ordinalClust ⋮ Bayesian model-based clustering for longitudinal ordinal data ⋮ Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series

Uses Software

• Latent GOLD
• AS 136
• Rankcluster
• CUB
• clusfind

Cites Work

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• A generative model for rank data based on insertion sort algorithm
• Algorithm AS 136: A K-Means Clustering Algorithm
• A mixture model for preferences data analysis
• Finite mixture models and model-based clustering
• Identifiability of parameters in latent structure models with many observed variables
• Estimating the dimension of a model
• An introduction to copulas. Properties and applications
• Modeling rating data with nonlinear CUB models
• Functional data clustering: a survey
• Model-based clustering for multivariate partial ranking data
• A theorem on CUB models for rank data
• A Clustering Method for Categorical Ordinal Data
• Latent Variable Models and Factor Analysis
• On the Theory of Scales of Measurement
• Finding Groups in Data
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Model-based clustering of Gaussian copulas for mixed data
• Exploratory latent structure analysis using both identifiable and unidentifiable models
• Measures of Association for Cross Classifications
• THE TREATMENT OF TIES IN RANKING PROBLEMS

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