Locally Weighted Ensemble Clustering

By Dong Huang, Changdong Wang, J. Lai. IEEE Transactions on Cybernetics, 2016

342 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/fa08cbf4db4fbcf790d5dfd9777ac6a1a0e5e8c2

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Citations in the corpus

Ultra-Scalable Spectral Clustering and Ensemble Clustering (2019, methodology): “7) LWGP [12]: locally weighted graph partitioning.”
Ensemble deep learning: A review (2021, background): “In recent years, some new ensemble clustering methods have been proposed that illustrated the priority of ensemble learning [133, 134, 135].”