Biclustering Via Sparse Clustering
This work addresses the need to identify homogeneous subgroups, such as in medical data for diseases like cancer, but it is incremental as it builds on existing sparse clustering methods.
The authors tackled the problem of identifying biclusters, which are subgroups of observations that differ only on a subset of features, by proposing a general framework based on sparse clustering. Their method showed favorable results compared to existing methods in terms of predictive accuracy and computing time on simulated and real-world datasets.
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.