Subset Clustering of Binary Sequences, with an Application to Genomic Abnormality Data

DOI10.1111/j.1541-0420.2005.00381.xzbMath1087.62125OpenAlexW2031109238WikidataQ31029095 ScholiaQ31029095MaRDI QIDQ3379257

Peter D. Hoff

Publication date: 6 April 2006

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2005.00381.x

zbMATH Keywords

unsupervised learningnonparametric Bayesmultivariate binary datagenetic pathway

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Medical applications (general) (92C50) Genetics and epigenetics (92D10)

Related Items (10)

Robust Naive Bayes Combination of Multiple Classifications ⋮ Cluster‐Specific Variable Selection for Product Partition Models ⋮ A Bayesian approach to restricted latent class models for scientifically structured clustering of multivariate binary outcomes ⋮ Variable Selection in Penalized Model‐Based Clustering Via Regularization on Grouped Parameters ⋮ Semi-supervised projected model-based clustering ⋮ Variable selection methods for model-based clustering ⋮ Penalized factor mixture analysis for variable selection in clustered data ⋮ Maximum likelihood estimation in constrained parameter spaces for mixtures of factor analyzers ⋮ A parametric mixture model for clustering multivariate binary data ⋮ Bayesian bi-clustering methods with applications in computational biology

Cites Work

• Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
• Ferguson distributions via Polya urn schemes
• Discovering Combinations of Genomic Aberrations Associated With Cancer
• Clustering Objects on Subsets of Attributes (with Discussion)
• Estimating normal means with a conjugate style dirichlet process prior
• A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion

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