计算机工程与应用2018，Vol.54Issue(5)：72-78,7.DOI:10.3778/j.issn.1002-8331.1610-0055
中心约束的跨源学习可能性C均值聚类算法
Central-constraints possibilistic C-means algorithms based on source domain
摘要
Abstract
Compared with Fuzzy C-Means(FCM),Possibilistic C-Means clustering algorithm(PCM)can deal with the data with noise and exception point better,but when dealing with the data set with strong viscosity,the clustering center of PCM algorithm will lead to the direct failure of clustering algorithm.To solve the above issue,this paper devises central-constraints and transfer based on source domain criterions,and applies these to PCM.It proposes Central-Constraints Possibilistic C-Means algorithms based on the Source Domain(CCSD_PCM for short),which can achieve better clustering effect.Improved algorithm can use the cross-domain knowledge to support the clustering,so as to guarantee the clustering performance of the algorithm.Through the simulation data sets and real data sets,it verifies the above-mentioned advantages of the algorithm.关键词
迁移学习/类中心约束/可能性C均值算法Key words
transfer learning/central-constraints/possibilistic C-means algorithms分类
信息技术与安全科学引用本文复制引用
夏洋洋,刘渊,黄亚东..中心约束的跨源学习可能性C均值聚类算法[J].计算机工程与应用,2018,54(5):72-78,7.基金项目
江苏省自然科学基金(No.BK20151131) （No.BK20151131）
中央高校基本科研业务费专项资金(No.JUSPR51614A). （No.JUSPR51614A）
