A unifying model for biclustering

DOI10.1007/978-3-7908-1709-6_7zbMath1437.62048OpenAlexW68142727MaRDI QIDQ3298589

Jan Schepers, Iven van Mechelen

Publication date: 15 July 2020

Published in: Compstat 2006 - Proceedings in Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-7908-1709-6_7

zbMATH Keywords

loss functionpartition matrixdata clusterhierarchical classisunify model

Mathematics Subject Classification ID

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

Related Items (5)

Mutual information, phi-squared and model-based co-clustering for contingency tables ⋮ A finite mixture approach to joint clustering of individuals and multivariate discrete outcomes ⋮ Biclustering multivariate discrete longitudinal data ⋮ Clustering microarray data using model-based doubleK-means ⋮ Multiple Nested Reductions of Single Data Modes as a Tool to Deal with Large Data Sets

Cites Work

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• Improved biclustering of microarray data demonstrated through systematic performance tests
• The real-valued model of hierarchical classes
• Optimising \(k\)-means clustering results with standard software packages
• A unifying model involving a categorical and/or dimensional reduction for multimode data
• Block clustering with Bernoulli mixture models: comparison of different approaches
• Hierarchical classes: model and data analysis
• Two-mode clustering methods: astructuredoverview

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