# On Hyperparameter Search in Cluster Ensembles

arXiv: https://arxiv.org/abs/1803.11008 · HTML: https://mlsanity.com/paper/1803.11008/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [DeepMoM: Robust Deep Learning With Median-of-Means](https://mlsanity.com/paper/2105.14035/) (2021)
- [Unsupervised Behaviour Analysis of News Consumption in Turkish Media](https://mlsanity.com/paper/2202.02056/) (2022)
- [Parallel Computation of Multi-Slice Clustering of Third-Order Tensors](https://mlsanity.com/paper/2309.17383/) (2023)

## Abstract (arXiv)

Quality assessments of models in unsupervised learning and clustering verification in particular have been a long-standing problem in the machine learning research. The lack of robust and universally applicable cluster validity scores often makes the algorithm selection and hyperparameter evaluation a tough guess. In this paper, we show that cluster ensemble aggregation techniques such as consensus clustering may be used to evaluate clusterings and their hyperparameter configurations. We use normalized mutual information to compare individual objects of a clustering ensemble to the constructed consensus of the whole ensemble and show, that the resulting score can serve as an overall quality measure for clustering problems. This method is capable of highlighting the standout clustering and hyperparameter configuration in the ensemble even in the case of a distorted consensus. We apply this very general framework to various data sets and give possible directions for future research.