A Semi-supervised Clustering Algorithm Based on Must-Link Set

被引：0
|
作者：
Huang, Haichao [1 ]
Cheng, Yong [1 ]
Zhao, Ruilian [1 ]
机构：
[1] Beijing Inst Chem Technol, Coll Informat Sci & Technol, Dept Comp, Beijing 100029, Peoples R China
来源：
ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS | 2008年 / 5139卷
关键词：
Semi-supervised Learning; Data Clustering; Constraint; MLC-KMeans Algorithm;
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：
Clustering analysis is traditionally considered as an unsupervised learning process. In most cases, people usually have some prior or background knowledge before they perform the clustering. How to use the prior or background knowledge to improve the cluster quality and promote the efficiency of clustering data has become a hot research topic in recent years. The Must-Link and Cannot-Link constraints between instances are common prior knowledge in many real applications. This paper presents the concept of Must-Link Set and designs a new semi-supervised clustering algorithm MLC-KMeans using Musk-Link Set as assistant centroid. The preliminary experiment on several UCI datasets confirms the effectiveness and efficiency of the algorithm.
引用
收藏
页码：492 / 499
页数：8
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