# Robust Ensemble Clustering Using Probability Trajectories

arXiv: https://arxiv.org/abs/1606.01160 · HTML: https://mlsanity.com/paper/1606.01160/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2016.

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

- [Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity](https://mlsanity.com/paper/2203.11572/) (2022)
- [Ensemble Clustering via Co-association Matrix Self-enhancement](https://mlsanity.com/paper/2205.05937/) (2022)
- [DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks](https://mlsanity.com/paper/2206.00359/) (2022)
- [k-HyperEdge Medoids for Clustering Ensemble](https://mlsanity.com/paper/2412.08289/) (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)
- [Generalization Performance of Ensemble Clustering: From Theory to Algorithm](https://mlsanity.com/paper/2506.02053/) (2025)
- [Robust Consensus Clustering and its Applications for Advertising Forecasting](https://mlsanity.com/paper/2301.00717/) (2022)

## Abstract (arXiv)

Although many successful ensemble clustering approaches have been developed in recent years, there are still two limitations to most of the existing approaches. First, they mostly overlook the issue of uncertain links, which may mislead the overall consensus process. Second, they generally lack the ability to incorporate global information to refine the local links. To address these two limitations, in this paper, we propose a novel ensemble clustering approach based on sparse graph representation and probability trajectory analysis. In particular, we present the elite neighbor selection strategy to identify the uncertain links by locally adaptive thresholds and build a sparse graph with a small number of probably reliable links. We argue that a small number of probably reliable links can lead to significantly better consensus results than using all graph links regardless of their reliability. The random walk process driven by a new transition probability matrix is utilized to explore the global information in the graph. We derive a novel and dense similarity measure from the sparse graph by analyzing the probability trajectories of the random walkers, based on which two consensus functions are further proposed. Experimental results on multiple real-world datasets demonstrate the effectiveness and efficiency of our approach.