# Weighted Clustering Ensemble: A Review

arXiv: https://arxiv.org/abs/1910.02433 · HTML: https://mlsanity.com/paper/1910.02433/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [Review of Clustering Methods for Functional Data](https://mlsanity.com/paper/2210.00847/) (2022)
- [Selective clustering ensemble based on kappa and F-score](https://mlsanity.com/paper/2204.11062/) (2022)
- [Similarity and Dissimilarity Guided Co-association Matrix Construction for Ensemble Clustering](https://mlsanity.com/paper/2411.00904/) (2024)
- [Robustness of Generalized Median Computation for Consensus Learning in Arbitrary Spaces](https://mlsanity.com/paper/2503.05215/) (2025)
- [Generalization Performance of Ensemble Clustering: From Theory to Algorithm](https://mlsanity.com/paper/2506.02053/) (2025)
- [fMBN-E: Efficient Unsupervised Network Structure Ensemble and Selection for Clustering](https://mlsanity.com/paper/2107.02071/) (2021)
- [SNAP: A Self-Consistent Agreement Principle with Application to Robust Computation](https://mlsanity.com/paper/2602.02013/) (2026)
- [CAKE: Confidence in Assignments via K-partition Ensembles](https://mlsanity.com/paper/2602.18435/) (2026)

## Abstract (arXiv)

Clustering ensemble, or consensus clustering, has emerged as a powerful tool for improving both the robustness and the stability of results from individual clustering methods. Weighted clustering ensemble arises naturally from clustering ensemble. One of the arguments for weighted clustering ensemble is that elements (clusterings or clusters) in a clustering ensemble are of different quality, or that objects or features are of varying significance. However, it is not possible to directly apply the weighting mechanisms from classification (supervised) domain to clustering (unsupervised) domain, also because clustering is inherently an ill-posed problem. This paper provides an overview of weighted clustering ensemble by discussing different types of weights, major approaches to determining weight values, and applications of weighted clustering ensemble to complex data. The unifying framework presented in this paper will help clustering practitioners select the most appropriate weighting mechanisms for their own problems.