Mutual information, phi-squared and model-based co-clustering for contingency tables

DOI10.1007/s11634-016-0274-6zbMath1416.62309OpenAlexW2552406463MaRDI QIDQ1630868

Gérard Govaert, Mohamed Nadif

Publication date: 5 December 2018

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-016-0274-6

zbMATH Keywords

information theorycontingency tablebiclusteringco-clustering

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Contingency tables (62H17)

Related Items

Semi-supervised latent block model with pairwise constraints, Unifying data units and models in (co-)clustering, Sparse correspondence analysis for large contingency tables, A finite mixture approach to joint clustering of individuals and multivariate discrete outcomes, Poisson degree corrected dynamic stochastic block model, On mathematical optimization for clustering categories in contingency tables, E-ReMI: extended maximal interaction two-mode clustering, A survey on model-based co-clustering: high dimension and estimation challenges, Noise-free latent block model for high dimensional data, Social regularized von Mises-Fisher mixture model for item recommendation, Efficient regularized spectral data embedding, Comparing high-dimensional partitions with the Co-clustering adjusted rand index, Model-based co-clustering for mixed type data, Regularized bi-directional co-clustering, Implicit consensus clustering from multiple graphs, Co-clustering of time-dependent data via the shape invariant model, Directional co-clustering

Cites Work

• Latent Block Model for Contingency Table
• Clustering the rows and columns of a contingency table
• MIB: using mutual information for biclustering gene expression data
• Two-mode multi-partitioning
• Block clustering with Bernoulli mixture models: comparison of different approaches
• Another interpretation of the EM algorithm for mixture distributions
• Parameter modification for clustering criteria
• Convexity-based clustering criteria: theory, algorithms, and applications in statistics
• A classification EM algorithm for clustering and two stochastic versions
• Clustering of contingency table and mixture model
• Block seriation problems: A unified approach. Reply to the problem of H. Garcia and J. M. Proth (Applied Stochastic Models and Data Analysis, 1, (1), 25–34 (1985))
• A unifying model for biclustering
• 10.1162/153244303321897735
• Two-mode clustering methods: astructuredoverview
• Concept decompositions for large sparse text data using clustering
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item