一种基于同类约束的半监督近邻反射传播聚类方法  

A Semi-supervised Affinity Propagation Clustering Method with Homogeneity Constraint

作　　者：徐明亮[1,2] 王士同[1] 杭文龙 

机构地区：[1]江南大学数字媒体学院,无锡214122 [2]无锡城市职业技术学院,无锡214153

出　　处：《自动化学报》2016年第2期255-269,共15页Acta Automatica Sinica

基　　金：国家自然科学基金(61170122,61202311,61272210);江苏省自然科学基金(BK2012552)资助

摘　　要：以近邻反射传播(Affinity propagation,AP)聚类算法为基础,提出了一种基于同类约束的半监督近邻反射传播聚类方法 (Semi-supervised affinity propagation clustering method with homogeneity constraints,HCSAP).该方法在聚类目标函数中引入同类约束项,以保证聚类结果与同类集先验信息一致.利用最大和信任传播(Max-sum belief propagation)优化过程对目标函数进行求解,导出同类约束下的吸引度(Responsibility)和归属度(Availability)的迭代方程.人工数据集和真实数据集上的实验结果表明本文所提方法的有效性.In this paper, a semi-supervised affinity propagation（AP） clustering algorithm with homogeneity constraint,called HCSAP（semi-supervised affinity propagation clustering method with homogeneity constraints）, is proposed. To keep consistency between the clustering results and the priori information about homogeneity sets, the constraint terms are introduced to the objection function of algorithm AP. With the max-sum belief propagation procedure, the objection function can be resolved into the corresponding responsibility and availability update equations. Experiments on synthetic dataset and real-world datasets indicate the effectiveness of the proposed HCSAP.

关 键 词：半监督聚类 近邻反射传播 最大和 信任传播 同类约束 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]