In cancer research, as in all of medicine, it is important to classify patients into etiologically and therapeutically relevant subtypes to improve diagnosis and treatment. One way to do this is to use clustering methods to find subgroups of homogeneous individuals based on genetic profiles together with heuristic clinical analysis. A notable drawback of existing clustering methods is that they ignore the possibility that the variance of gene expression profile measurements can be heterogeneous across subgroups, and methods that do not consider heterogeneity of variance can lead to inaccurate subgroup prediction. Research has shown that hypervariability is a common feature among cancer subtypes. In this paper, we present a statistical approach that can capture both mean and variance structure in genetic data. We demonstrate the strength of our method in both synthetic data and in two cancer data sets. In particular, our method confirms the hypervariability of methylation level in cancer patients, and it detects clearer subgroup patterns in lung cancer data.C lustering is an important type of unsupervised learning algorithm for data exploration. Successful examples include Kmean clustering and hierarchical clustering, both of which are widely used in biological research to find cancer subtypes and to stratify patients. These and other traditional clustering algorithms depend on the distances calculated using all of the features. For example, individuals can be clustered into homogeneous groups by minimizing the summation of within-clusters sum of squares (the Euclidean distances) of their gene expression profiles. Unfortunately, this strategy is ineffective when only a subset of features is informative. This phenomenon can be demonstrated by K-means clustering (1) results for a toy example using only the variables which determine the underlying true cluster compared with using all variables (which includes many uninformative variables). As can be seen in Fig. 1, clustering performance is poor when all variables are used in the clustering algorithm (2).To solve this problem, sparse clustering methods have been proposed to allow clustering decisions to depend on only a subset of feature variables (the property of sparsity). Prominent sparse clustering methods include sparse principal component analysis (PCA) (3-5) and Sparse K-means (2), among others (6). However, sparse clustering still fails if the true sparsity is a local rather than a global phenomenon (6). More specifically, different subsets of features can be informative for some samples but not all samples, or, in other words, sparsity exists in both features and samples jointly. Biclustering methods are a potential solution to this problem, and further generalize the sparsity principle by considering samples and features as exchangeable concepts to handle local sparsity (6, 7). For example, gene expression data can be represented as a matrix with genes as columns, and subjects as rows (with various and possibly unknown diseases or tissue types). Traditiona...