Multi-view data, which represent distinct but related groupings of variables, can be useful for identifying relevant and robust clustering structures among observations. A large number of multi-view classification algorithms have been proposed in the fields of computer science and ge-nomics; in this work, we instead focus on the task of merging or splitting an existing hard or fuzzy cluster partition based on multi-view data. This work is specifically motivated by an application involving multi-omic breast cancer data from The Cancer Genome Atlas, where multiple molecular profiles (gene expression, miRNA expression, methylation, and copy number alterations) are used to further subdivide the five currently accepted intrinsic tumor subtypes into clinically distinct subgroups of patients. In addition, we investigate the performance of the proposed multi-view splitting and aggregation algorithms, as compared to single-and concatenated-view alternatives , in a set of simulations. The multi-view splitting and aggregation algorithms developed in this work are implemented in the maskmeans R package.