Tri-level Robust Clustering Ensemble with Multiple Graph Learning

Authors

  • Peng Zhou School of Computer Science and Technology, Anhui University State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences
  • Liang Du School of Computer and Information Technology, Shanxi University
  • Yi-Dong Shen State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences
  • Xuejun Li School of Computer Science and Technology, Anhui University

Keywords:

Clustering, Ensemble Methods

Abstract

Clustering ensemble generates a consensus clustering result by integrating multiple weak base clustering results. Although it often provides more robust results compared with single clustering methods, it still suffers from the robustness problem if it does not treat the unreliability of base results carefully. Conventional clustering ensemble methods often use all data for ensemble, while ignoring the noises or outliers on the data. Although some robust clustering ensemble methods are proposed, which extract the noises on the data, they still characterize the robustness in a single level, and thus they cannot comprehensively handle the complicated robustness problem. In this paper, to address this problem, we propose a novel Tri-level Robust Clustering Ensemble (TRCE) method by transforming the clustering ensemble problem to a multiple graph learning problem. Just as its name implies, the proposed method tackles robustness problem in three levels: base clustering level, graph level and instance level. By considering the robustness problem in a more comprehensive way, the proposed TRCE can achieve a more robust consensus clustering result. Experimental results on benchmark datasets also demonstrate it. Our method often outperforms other state-of-the-art clustering ensemble methods. Even compared with the robust ensemble methods, ours also performs better.

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Published

2021-05-18

How to Cite

Zhou, P., Du, L., Shen, Y.-D., & Li, X. (2021). Tri-level Robust Clustering Ensemble with Multiple Graph Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(12), 11125-11133. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17327

Issue

Section

AAAI Technical Track on Machine Learning V