摘要
提出了一种基于决策粗糙集的面向知识的自动聚类方法。在面向知识的聚类算法中,获取初始聚类结果依赖人工阈值的设置。为此,首先根据物理学知识提出了一种差值排序方法来自动得到阈值。另外,讨论了决策粗糙集模型的损失函数,提出了一种聚类评估方法;通过对聚类结果的评估来实现自动聚类。实验结果表明新方法是有效的。
This paper proposed an autonomous knowledge-oriented clustering method based on decision-theoretic rough set model.In order to obtain the initial clustering,the initial threshold values need to set in the knowledge-oriented clustering framework.Thus,a novel method,sort difference,was proposed to produce the initial threshold values autonomously in view of physics theory.Then,a cluster validity index based on the decision-theoretic rough set model was developed by considering various loss functions,which can estimate the quality of clustering.The results of experiments show that the new approach is valuable.
出处
《计算机科学》
CSCD
北大核心
2011年第1期221-224,共4页
Computer Science
基金
重庆市科委项目(CSTC
2009BB2082)
重庆市教委项目(KJ080510)资助
关键词
聚类
面向知识
决策粗糙集
自动
Clustering
Knowledge-oriented
Decision-theoretic rough set
Autonomous
