摘要
提出了一种结合了基于密度聚类思想的划分聚类方法——"密度最大值聚类算法(MDCA)",以最大密度对象作为起始点,通过考察最大密度对象所处空间区域的密度分布情况来划分基本簇,并合并基本簇获得最终的簇划分.实验表明,MDCA能够自动确定簇数量,并有效发现任意形状的簇,对于未知数据集的处理能力和聚类准确度都优于传统的基于划分聚类算法.
This paper proposes a new clustering algorithm named maximum density clustering algorithm（MDCA）. In MDCA the concept of density is introduced to identify the count of clusters automatically. By selecting the densest object as the threshold, densities of those objects around the densest object are reviewed to decide the partition of basic blocks. Then the basic blocks are merged to form clusters of arbitrary shape. Experiments show that the ability and validity of MDCA in processing unknown datasets are all better than traditional partition-based clustering algorithms.
出处
《中国科学院研究生院学报》
CAS
CSCD
北大核心
2009年第4期539-548,共10页
Journal of the Graduate School of the Chinese Academy of Sciences
基金
国家863计划(2006AA01Z454)
电子信息产业发展基金资助
