  In many situations it is desirable to identify clusters that differ with
respect to only a subset of features. Such clusters may represent homogeneous
subgroups of patients with a disease, such as cancer or chronic pain. We define
a bicluster to be a submatrix U of a larger data matrix X such that the
features and observations in U differ from those not contained in U. For
example, the observations in U could have different means or variances with
respect to the features in U. We propose a general framework for biclustering
based on the sparse clustering method of Witten and Tibshirani (2010). We
develop a method for identifying features that belong to biclusters. This
framework can be used to identify biclusters that differ with respect to the
means of the features, the variance of the features, or more general
differences. We apply these methods to several simulated and real-world data
sets and compare the results of our method with several previously published
methods. The results of our method compare favorably with existing methods with
respect to both predictive accuracy and computing time.
