Unsupervised clustering is important in disease subtyping, among having other genomic applications. As genomic data has become more multifaceted, how to cluster across data sources for more precise subtyping is an ever more important area of research. Many of the methods proposed so far, including iCluster and Cluster of Cluster Assignments, make an unreasonble assumption of a common clustering across all data sources, and those that do not are fewer and tend to be computationally intensive.