一种基于同类约束的半监督近邻反射传播聚类方法

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.

A Semi-supervised Affinity Propagation Clustering Method with Homogeneity Constraint

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.