微型机与应用2012，Vol.31Issue(24)：67-69,3.
基于成对约束的半监督凝聚层次聚类算法
Semi-supervised agglomerative hierarchical clustering based pairwise constraints
盛俊杰 1谢丽聪1
作者信息
- 1. 福州大学数学与计算机学院,福建福州350108
- 折叠
摘要
Abstract
Semi-supervised clustering uses the samples' supervised information to aid unsupervised learning. In the semi-su- pervised clustering, pairwise constraints information (must-link constraints and cannot-link constraints) are widely used as samples' prior knowledge. Agglomerative hierarchical clustering (AHC) is one kind of hierarchical clustering .This paper presents a semi-supervised agglomerative hierarchical clustering algorithm based on pairwise constraints (PS-AHC). The algorithm uses pairwise constraints to change distances of clusters. It makes distances of clusters closer to the truth. The results of experiments on the UCI data sets confirm that PS-AHC algorithm can improve the accuracy of clustering effectively and that it is a promising semi-supervised clustering algorithm.关键词
半监督聚类/成对约束/凝聚层次聚类Key words
semi-supervised clustering/pairwise constraints/agglomerative hierarchical clustering分类
信息技术与安全科学引用本文复制引用
盛俊杰,谢丽聪..基于成对约束的半监督凝聚层次聚类算法[J].微型机与应用,2012,31(24):67-69,3.
