摘要
聚类集成通过对原始数据集的多个聚类结果进行学习和集成,得到一个能较好地反映数据集内在结构的数据划分。聚类集成能够较好地检测和处理孤立点,提高聚类结果质量。综述了聚类集成的相关知识,介绍了聚类集成的相关概念和优点;根据使用的聚类算法介绍了3种产生聚类成员方法,分析了各自的优缺点及适用条件;介绍了目前已有的一致性函数,阐述了其基本原理,并指出了其局限;最后讨论了未来的研究方向。
Clustering aggregation can offer a partition that could better reflect the inherent structure of the data set by studying and integrating many clustering results of the original data set.Clustering aggregation could detect and deal with the isolated points preferably,which improves the quality of clustering.This paper made an overview of the relevant knowledge of the clustering aggregation,presented the concepts and advantages of clustering aggregation.It pre-sented three approaches to get clustering members according to the uesd cluster algorithms;analysed their respective advantages,disadvantages and application conditions;presented the existing consensus functions;explained the basic principles and pointed out their limitations.Finally,it discussed the future research directions.
出处
《计算机科学》
CSCD
北大核心
2011年第2期166-170,共5页
Computer Science
基金
国家自然科学基金项目(60773099
60873149
60973088)
国家863高技术研究发展计划项目(2006AA10Z245
2006AA10A309)
中央高校基本科研业务费专项资金资助。
关键词
聚类集成
聚类成员
一致性函数
聚类算法
Clustering aggregation
Clustering member
Consensus function
Clustering algorithms
