Abstract This article introduces an agglomerative Bayesian model-based clustering algorithm which outputs a nested sequence of two-way cluster configurations for an input matrix of data. Each two-way cluster configuration in the output hierarchy is specified by a row configuration and a column configuration whose Cartesian product partitions the data matrix. Variable selection is incorporated into the algorithm by identifying row clusters which form distinct groups defined by the column cluster.
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