Abstract
A new agglomerative method is proposed for the simultaneous hierarchical clustering of row and column elements of a two-mode data matrix. The procedure yields a nested sequence of partitions of the union of two sets of entities (modes). A two-mode cluster (bi-cluster) is defined as the union of subsets of the respective modes. At each step of the agglomerative process, the algorithm merges two bi-clusters whose fusion results in the minimum increase in an internal heterogeneity measure. This measure takes into account both the variance within a bi-cluster and its elevation defined as the squared deviation of its mean from the maximum entry in the original matrix. Two applications concerning brand-switching data and gender subtype-situation matching data are discussed.
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