计算机工程与应用2011，Vol.47Issue(8)：143-145,3.DOI:10.3778/j.issn.1002-8331.2011.08.042
基于区域比例的聚类方法
Clustering algorithm based on local scaling.
李伟雄 1谭建豪 1王贵山1
作者信息
- 1. 湖南大学,电气与信息工程学院,长沙,410082
- 折叠
摘要
Abstract
In order to improve the robustness of parameters and undergrade clustering quality when clustering dataset with maldistribution of density,the paper presents an improved clustering algorithm based on DBSCAN.The algorithm uses average distance of knn objects to measure the density of each object. The center object is defined as the object of local maximal density,the seed expands from center point until the edge of density which is defined by scale-factor of density. Candidate core object is used to improve the quality of clustering by searching core object with scale-factor of radius. In experiment,datasets are used to test the algorithm,which proves the robustness of parameters of the improved algorithm and excellent performance when clustering dataset with maldistribution of density.关键词
基于密度的带噪声应用的空间聚类方法(DBSCAN)/聚类算法/密度/区域比例Key words
Density-Based Spatial Clustering of Applications with Noise(DBSCAN)/ clustering/ density/ local scaling分类
信息技术与安全科学引用本文复制引用
李伟雄,谭建豪,王贵山..基于区域比例的聚类方法[J].计算机工程与应用,2011,47(8):143-145,3.
