A Self-trained Semi Supervised Fuzzy Clustering Based on Label Propagation with Variable Weights

被引:0
|
作者
Zheng, Jiannan [1 ]
Zhou, Yuling [2 ]
Deng, Tian [2 ]
Yang, Xiyang [2 ]
机构
[1] Fujian Elect Power Tech Coll, Dept Comprehens Fdn Studies, Quanzhou 362000, Peoples R China
[2] Fujian Prov Key Lab Data Intens Comp, Quanzhou 362000, Peoples R China
来源
2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2017年
关键词
semi-supervised; fuzzy clustering; label propagation; PAIRWISE CONSTRAINTS; SEMISUPERVISED SVM; CLASSIFICATION;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Clustering accuracy of fuzzy clustering is sensitive to the structure of dataset to be studied. Semi-supervised clustering algorithms aim to increase the accuracy under the supervisions of a limited amount of labeled data, but the classification rate is highly dependent on the size of available labeled data. To overcome this disadvantage, we propose a novel semi-supervised clustering based on label propagation. Under our label propagation mechanism, an unlabeled datum propagates an estimated label from two aspects: (1) from its adjacent labeled data; (2) from a previous clustering result. The effects of these estimated labels are controlled by weights indicating their confidence levels. The effectiveness of the proposed model with label propagation scheme are evaluated by several real-life data sets. Experimental results show that accuracy level would increase by applying this learning scheme, compared to other semi-supervised models.
引用
收藏
页码:7447 / 7452
页数:6
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