计算机应用研究2017，Vol.34Issue(10)：2938-2941,2992,5.DOI:10.3969/j.issn.1001-3695.2017.10.013
一种基于扩展区域查询的密度聚类算法
Density clustering algorithm based on extended range query
摘要
Abstract
There are several troublesome limitations of DBSCAN:a)parameters have to be set;b)the time consumption is intolerable in expansion;c)it is sensitive to the density of starting points;d)it is difficult to identify the adjacent clusters of different densities.This paper proposed an enhanced and efficient density clustering algorithm based on extended range query named GISN-DBSCAN.Firstly,it proposed an extended range query algorithm based on fixed-grids to reduce the time overhead of searching the nearest neighborhood.Then it used the nearest neighbors and reverse nearest neighbors to establish the k-influence space neighborhood of each point.Finally,it presented a computational method of k-outlierness function to distinguish the border points and noise points accurately.Experimental results demonstrate that GISN-DBSCAN can address the drawbacks of DBSCAN algorithm and identify the border points and noise points effectively.关键词
密度聚类算法/扩展区域查询/k-影响空间域/边界点检测Key words
density clustering algorithm/extended range query/k-influence space neighborhood/border points detection分类
信息技术与安全科学引用本文复制引用
杨杰明,吴启龙,曲朝阳,张慧莉,蔺洪文,吕正卓..一种基于扩展区域查询的密度聚类算法[J].计算机应用研究,2017,34(10):2938-2941,2992,5.基金项目
国家自然科学基金资助项目(51277023) （51277023）
吉林省科技发展计划资助项目(20140204071GX) （20140204071GX）
