Locally Weighted Ensemble Clustering

• Published in: IEEE transactions on cybernetics Vol. 48; no. 5; pp. 1460 - 1473
• Main Authors: Huang, Dong, Wang, Chang-Dong, Lai, Jian-Huang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cluster uncertainty estimation; Clustering; Clustering algorithms; consensus clustering; ensemble clustering; Estimation; Indexes; local weighting; Partitioning algorithms; Reliability analysis; Robustness; Uncertainty
• DOI: 10.1109/TCYB.2017.2702343
• ISSN: 2168-2267; 2168-2275

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.