ABSTRACT: Multi-view clustering methods are essential for the stratification of patients into sub-groups of similar molecular characteristics. In recent years, a wide
range of methods has been developed for this purpose. However, due to the high diversity of cancer-related data, a single method may not perform sufficiently well in
all instances. We present a multi-view hierarchical ensemble clustering framework of
methods. We apply and validate it on real-world multi-view cancer patient data. Our
approach outperforms the current state-of-the-art in all but one cases. It is integrated in our Python package Pyrea [https://github.com/mdbloice/Pyrea].