TY - JOUR
T1 - Transfer clustering ensemble selection
AU - Shi, Yifan
AU - Yu, Zhiwen
AU - Chen, C. L. Philip
AU - You, Jia
AU - Wong, Hau-San
AU - Wang, Yide
AU - Zhang, Jun
PY - 2020/6
Y1 - 2020/6
N2 - 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.
AB - 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.
U2 - 10.1109/TCYB.2018.2885585
DO - 10.1109/TCYB.2018.2885585
M3 - Journal article
VL - 50
SP - 2872
EP - 2885
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
SN - 2168-2267
IS - 6
ER -