# Locally Weighted Ensemble Clustering

arXiv: https://arxiv.org/abs/1605.05011 · HTML: https://mlsanity.com/paper/1605.05011/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2016.

## Built on by (most cited first, 19 of 19)

- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity](https://mlsanity.com/paper/2203.11572/) (2022)
- [DivClust: Controlling Diversity in Deep Clustering](https://mlsanity.com/paper/2304.01042/) (2023)
- [Strongly Augmented Contrastive Clustering](https://mlsanity.com/paper/2206.00380/) (2022)
- [Joint Multi-view Unsupervised Feature Selection and Graph Learning](https://mlsanity.com/paper/2204.08247/) (2022)
- [Ensemble Clustering via Co-association Matrix Self-enhancement](https://mlsanity.com/paper/2205.05937/) (2022)
- [Deep Temporal Contrastive Clustering](https://mlsanity.com/paper/2212.14366/) (2022)
- [Self-supervised Symmetric Nonnegative Matrix Factorization](https://mlsanity.com/paper/2103.01689/) (2021)
- [DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks](https://mlsanity.com/paper/2206.00359/) (2022)
- [Deep Clustering With Consensus Representations](https://mlsanity.com/paper/2210.07063/) (2022)
- [Heterogeneous Tri-stream Clustering Network](https://mlsanity.com/paper/2301.04451/) (2023)
- [Comparing the information content of probabilistic representation spaces](https://mlsanity.com/paper/2405.21042/) (2024)
- [Similarity and Dissimilarity Guided Co-association Matrix Construction for Ensemble Clustering](https://mlsanity.com/paper/2411.00904/) (2024)
- [LSEC: Large-scale spectral ensemble clustering](https://mlsanity.com/paper/2106.09852/) (2021)
- [OOG- Optuna Optimized GAN Sampling Technique for Tabular Imbalanced Malware Data](https://mlsanity.com/paper/2212.01274/) (2022)
- [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)
- [Cluster trajectory of SOFA score in predicting mortality in sepsis](https://mlsanity.com/paper/2311.17066/) (2023)
- [Crowdsourcing Without People: Modelling Clustering Algorithms as Experts](https://mlsanity.com/paper/2509.25395/) (2025)

## Abstract (arXiv)

Due to its ability to combine multiple base clusterings into a probably better and more robust clustering, the ensemble clustering technique has been attracting increasing attention in recent years. Despite the significant success, one limitation to most of the existing ensemble clustering methods is that they generally treat all base clusterings equally regardless of their reliability, which makes them vulnerable to low-quality base clusterings. Although some efforts have been made to (globally) evaluate and weight the base clusterings, yet these methods tend to view each base clustering as an individual and neglect the local diversity of clusters inside the same base clustering. It remains an open problem how to evaluate the reliability of clusters and exploit the local diversity in the ensemble to enhance the consensus performance, especially in the case when there is no access to data features or specific assumptions on data distribution. To address this, in this paper, we propose a novel ensemble clustering approach based on ensemble-driven cluster uncertainty estimation and local weighting strategy. In particular, the uncertainty of each cluster is estimated by considering the cluster labels in the entire ensemble via an entropic criterion. A novel ensemble-driven cluster validity measure is introduced, and a locally weighted co-association matrix is presented to serve as a summary for the ensemble of diverse clusters. With the local diversity in ensembles exploited, two novel consensus functions are further proposed. Extensive experiments on a variety of real-world datasets demonstrate the superiority of the proposed approach over the state-of-the-art.