  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 cases. We present Parea, a multi-view hierarchical
ensemble clustering approach for disease subtype discovery. We demonstrate its
performance on several machine learning benchmark datasets. We apply and
validate our methodology on real-world multi-view cancer patient data. Parea
outperforms the current state-of-the-art on six out of seven analysed cancer
types. We have integrated the Parea method into our developed Python package
Pyrea (https://github.com/mdbloice/Pyrea), which enables the effortless and
flexible design of ensemble workflows while incorporating a wide range of
fusion and clustering algorithms.
