期刊
KNOWLEDGE-BASED SYSTEMS
卷 163, 期 -, 页码 227-240出版社
ELSEVIER
DOI: 10.1016/j.knosys.2018.08.034
关键词
Unsupervised learning; Clustering; Plane-based clustering; Twin support vector clustering; Twin support vector machine
资金
- National Natural Science Foundation of China [11501310, 61703370, 61866010, 11871183]
- Inner Mongolia Autonomous Region university scientific research project [NJZC17006]
- Natural Science Foundation of Zhejiang Province [LQ17F030003]
- Hainan Provincial Natural Science Foundation of China [118QN181]
- Scientific Research Foundation of Hainan University [kyqd(sk)1804]
The recently proposed twin support vector clustering (TWSVC) is a powerful clustering method. However, TWSVC may encounter the singularity problem and it is time consuming in the learning stage. In this paper, we introduce some efficient techniques into TWSVC, and propose two clustering models, called twin bounded support vector clustering (TBSVC) and least square twin bounded support vector clustering (LSTBSVC), respectively. TBSVC introduces a maximum margin regularization term into TWSVC, which not only avoids its singularity but also significantly improves the performance. LSTBSVC introduces the least square formation into TBSVC to greatly accelerate its learning speed. Moreover, a uniform output coding for LSTBSVC is introduced to cope with the non-uniformed problem in the learning procedures. In addition, nonlinear clustering is also extended to the above clustering methods by using the kernel trick. Experimental results show the effectiveness and efficiency of our methods. (C) 2018 Elsevier B.V. All rights reserved.
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