摘要
作为目前聚类分析的新兴研究热点,聚类组合方法能将两种或多种聚类方法集成起来以改善其性能。从聚类多样性和共识函数两方面综述了最新研究进展,探讨将神经网络组合的思想用于聚类组合。最后指出了将来可能的研究方向。
As a novel research hotspot of clustering analysis currently,clustering ensemble can improve the performance of data clustering by combining two or muhiple clustering algorithms.Some latest research results on clustering diversity and consensus function are reviewed,and a clustering ensemble method inspired by neural network ensemble is presented.Finally the future research issues are discussed.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第11期142-144,共3页
Computer Engineering and Applications
基金
四川省重大基础研究子项目(the Key Basic Application Foundation of Sichuan Province under Grant No.04JY029-001-4)
关键词
聚类组合
多样性分量
共识函数
神经网络组合
Clustering ensemble
diversity component
consensus function
neural network ensemble