Semi-supervised Agglomerative Hierarchical Clustering Using Clusterwise Tolerance Based Pairwise Constraints

被引：0
|
作者：
Hamasuna, Yukihiro [1 ]
Endo, Yasunori [1 ]
Miyamoto, Sadaaki [1 ]
机构：
[1] Univ Tsukuba, Fac Syst & Informat Engn, Dept Risk Engn, Tsukuba, Ibaraki 3058573, Japan
来源：
MODELING DECISIONS FOR ARTIFICIAL INTELLIGENCE (MDAI) | 2010年 / 6408卷
关键词：
semi-supervised clustering; agglomerative hierarchical clustering; centroid method; clusterwise tolerance; pairwise constraints;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Recently, semi-supervised clustering has been remarked and discussed in many researches. In semi-supervised clustering, pairwise constraints, that is, must-link and cannot-link are frequently used in order to improve clustering results by using prior knowledges or informations. In this paper, we will propose a clusterwise tolerance based pairwise constraint. In addition, we will propose semi-supervised agglomerative hierarchical clustering algorithms with centroid method based on it. Moreover, we will show the effectiveness of proposed method through numerical examples.
引用
收藏
页码：152 / 162
页数：11
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