# Parea: multi-view ensemble clustering for cancer subtype discovery

arXiv: https://arxiv.org/abs/2209.15399 · HTML: https://mlsanity.com/paper/2209.15399/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 34 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 1 of 1)

- [mvlearn: Multiview Machine Learning in Python](https://mlsanity.com/paper/2005.11890/) (2020)

## Built on by (most cited first, 7 of 7)

- [Federated unsupervised random forest for privacy-preserving patient stratification](https://mlsanity.com/paper/2401.16094/) (2024)
- [Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping](https://mlsanity.com/paper/2404.17886/) (2024)
- [Hybrid Ensemble Deep Graph Temporal Clustering for Spatiotemporal Data](https://mlsanity.com/paper/2409.12590/) (2024)
- [Advanced Unsupervised Learning: A Comprehensive Overview of Multi-View Clustering Techniques](https://mlsanity.com/paper/2512.05169/) (2025)
- [Robust Node Affinities via Jaccard-Biased Random Walks and Rank Aggregation](https://mlsanity.com/paper/2603.05375/) (2026)
- [DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data](https://mlsanity.com/paper/2307.04075/) (2023)
- [Community Detection in Networks: A Rough Sets and Consensus Clustering Approach](https://mlsanity.com/paper/2406.12412/) (2024)

## Abstract (arXiv)

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.