Constraint projection for semi-supervised cluster ensemble

被引：0
|
作者：
Gou, Zhijian [1 ,2 ]
机构：
[1] School of Computer Science and Engineering, University of Electronic Science and Technology, No. 4, Section 2, North Jianshe Road, Chengdu, China
[2] Institute of Information Security Engineering, Chengdu University of Information Technology, No. 24, Section 1, Xuefu Road, Economic Development Zone, Chengdu, China
来源：
ICIC Express Letters | 2015年 / 9卷 / 08期
关键词：
Clustering algorithms - Learning algorithms;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Semi-supervised cluster ensemble takes the advantages of ensemble learning and semi-supervised learning. In this paper, constraint (cannot-link and must-link) projections are illustrated for semi-supervised cluster ensemble (CPSCE), a hierarchical semi-supervised cluster ensemble algorithm. It is flexible for the relaxation of some constraints during the learning stage. First, the data points of instance-level constraints and base clustering ensemble results are together projected in a lower dimensional space guided by the constraints. Then, mesh partition software (METIS) is performed on the similarity matrix. Finally, a few datasets are chosen for experimentation from the UCI machine learning repository. The results show that CPSCE performs better than some existing algorithms. © 2015 ICIC International.
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页码：2319 / 2325
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