Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 32, Issue 4, Pages 1497-1511Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2020.2984814
Keywords
Learning systems; Linear programming; Computer science; Task analysis; Clustering methods; Diversity reception; Training data; Clustering ensemble; consensus learning; self-paced learning
Categories
Funding
- National Natural Science Fund of China [61806003, 61976129, 61922088, 61976205, 61772373, 61972001]
- Key Natural Science Project of the Anhui Provincial Education Department [KJ2018A0010]
Ask authors/readers for more resources
This study proposes a novel self-paced clustering ensemble method that gradually involves instances from easy to difficult ones into ensemble learning. It integrates the evaluation of the difficulty of instances and ensemble learning into a unified framework to automatically estimate the difficulty of instances and ensemble the base clusterings, obtaining an effective consensus clustering result.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available