Abstract
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 language | English |
---|---|
Article number | 8588377 |
Pages (from-to) | 2872 - 2885 |
Number of pages | 14 |
Journal | IEEE Transactions on Cybernetics |
Volume | 50 |
Issue number | 6 |
DOIs | |
Publication status | Published - Jun 2020 |
Keywords
- Clustering ensemble selection (CES)
- machine learning
- multiobjective
- transfer learning
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Information Systems
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering