Transfer clustering ensemble selection

Yifan Shi, Zhiwen Yu, C. L. Philip Chen, Jia You, Hau-San Wong, Yide Wang, Jun Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review


Clustering ensemble (CE) takes multiple clustering solutions into consideration in order to effectively improve the accuracy and robustness of the final result. To reduce redundancy as well as noise, a CE selection (CES) step is added to further enhance performance. Quality and diversity are two important metrics of CES. However, most of the CES strategies adopt heuristic selection methods or a threshold parameter setting to achieve tradeoff between quality and diversity. In this paper, we propose a transfer CES (TCES) algorithm which makes use of the relationship between quality and diversity in a source dataset, and transfers it into a target dataset based on three objective functions. Furthermore, a multiobjective self-evolutionary process is designed to optimize these three objective functions. Finally, we construct a transfer CE framework (TCE-TCES) based on TCES to obtain better clustering results. The experimental results on 12 transfer clustering tasks obtained from the 20newsgroups dataset show that TCE-TCES can find a better tradeoff between quality and diversity, as well as obtaining more desirable clustering results.
Original languageEnglish
Pages (from-to)2872 - 2885
JournalIEEE Transactions on Cybernetics
Issue number6
Publication statusPublished - Jun 2020


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