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Bayesian co‐clustering

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Co‐clustering means simultaneously identifying natural clusters in different kinds of objects. Examples include simultaneously clustering customers and products for a recommender application; simultaneously clustering proteins and molecules in microbiology; or simultaneously clustering documents and words in a text mining application. Important insights into a problem can be gained by understanding the interactions between clusters for the different kinds of objects. This paper considers Bayesian models for co‐clustering. The Bayesian approach begins by developing a model for the data generating process, and inverting that model through Bayesian inference to infer cluster membership, learn characteristics of the clusters, and fill in missing observations. We consider a basic Bayesian clustering model and several extensions to the model. Experimental evaluations and comparisons among the clustering methods are presented. WIREs Comput Stat 2015, 7:347–356. doi: 10.1002/wics.1359 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Statistical and Graphical Methods of Data Analysis > Nonparametric Methods
Original data matrix (a); reordered to reveal co‐clusters (b).
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Hierarchical axis‐aligned partition (a) and corresponding grid partition (b).
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Generative models for nonparametric Bayesian co‐clustering (NBCC) (a) and feature‐enriched nonparametric Bayesian co‐clustering (FE‐NBCC) (b).
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Graphical model for Bayesian co‐clustering.
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Statistical and Graphical Methods of Data Analysis > Nonparametric Methods
Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory
Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification

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