摘要
提出了一种混合约束的半监督聚类算法HCSCAP,综合考虑了已标号点和成对点约束信息,使2类先验信息在聚类的过程中能发挥各自的作用.通过调整相似性矩阵添加成对点约束,已标号点以宏结点的方式添加到相似性矩阵.给出了具体的算法步骤并进行了测试,实验表明:HCSCAP比只利用成对点约束信息的SAP算法和只利用标号点的SS-CAP算法的CRI指标要好,聚类簇数也更接近实际给定的类数.
A hybrid constrained semi-supervised clustering algorithm(HCSCAP) is proposed on the base of soft-constraint affinity propagation algorithm.In order to get a better clustering result,both labeled data and pair-wise constraints are considered in clustering to make use of two types of prior knowledge supplementary to each other.We exploit pair-wise constraints by adjusting the similarity matrix,and append labeled data as macro-nodes to the similarity matrix.The experiments show that the performance of HCSCAP is better than that of SAP which makes use of pair-wise constraints only and that of SSCAP which makes use of labeled data only.The number of clusters given by HCSCAP is more close to the actual class number.
出处
《烟台大学学报(自然科学与工程版)》
CAS
北大核心
2011年第4期298-303,共6页
Journal of Yantai University(Natural Science and Engineering Edition)
基金
国家自然科学基金资助项目(61070118)
山东省高等学校科技计划资助项目(J10LG27)
关键词
半监督聚类
混合约束
成对点约束
semi-supervised clustering
hybrid constrained
pair-wise constraints