| 一种基于密度最大值的聚类算法 |
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| 引用本文： | 王晶,夏鲁宁,荆继武.一种基于密度最大值的聚类算法[J].中国科学院研究生院学报,2009,26(4):539-548. |
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| 作者姓名： | 王晶 夏鲁宁 荆继武 |
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| 作者单位： | 1. 中国科学技术大学电子工程与信息科学系,合肥230027;中国科学院研究生院信息安全国家重点实验室,北京100049
2. 中国科学院研究生院信息安全国家重点实验室,北京,100049 |
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| 基金项目： | 国家高技术研究发展计划(863计划),电子信息产业发展基金 |
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| 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.
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| 关 键 词： | 数据挖掘 聚类 最大密度对象 k-means DBSCAN |
| 收稿时间： | 2008-10-08 |
| 修稿时间： | 2009-01-09 |
Maximum density clustering algorithm |
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| WANG Jing,XIA Lu-Ning,JING Ji-Wu.Maximum density clustering algorithm[J].Journal of the Graduate School of the Chinese Academy of Sciences,2009,26(4):539-548. |
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| Authors: | WANG Jing XIA Lu-Ning JING Ji-Wu |
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| Institution: | 1. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China;
2. State Key Lab of Information Security, Graduate University of the Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | 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. |
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| Keywords: | K-means DBSCAN |
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