Self-Paced Clustering Ensemble

Peng Zhou

doi:10.1109/TNNLS.2020.2984814

Self-Paced Clustering Ensemble

2021-04-01

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AI

DOI：10.1109/TNNLS.2020.2984814

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The clustering ensemble has emerged as an important extension of the classical clustering problem. It provides an elegant framework to integrate multiple weak base clusterings to generate a strong consensus result. Most existing clustering ensemble methods usually exploit all data to learn a consensus clustering result, which does not sufficiently consider the adverse effects caused by some difficult instances. To handle this problem, we propose a novel self-paced clustering ensemble (SPCE) method, which gradually involves instances from easy to difficult ones into the ensemble learning. In our method, we integrate the evaluation of the difficulty of instances and ensemble learning into a unified framework, which can automatically estimate the difficulty of instances and ensemble the base clusterings. To optimize the corresponding objective function, we propose a joint learning algorithm to obtain the final consensus clustering result. Experimental results on benchmark data sets demonstrate the effectiveness of our method.

Keywords:

Learning systems

Linear programming

Computer science

Task analysis

Clustering methods

Diversity reception

Training data

Clustering ensemble

consensus learning

self-paced learning

Self-Paced Clustering Ensemble

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