《计算机应用研究》|Application Research of Computers

基于方形邻域的网格密度聚类算法

Grid density clustering algorithm based on square neighborhood

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作者 兰红,朱合隆
机构 江西理工大学 信息工程学院,江西 赣州 341400
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2020)06-026-1735-06
DOI 10.19734/j.issn.1001-3695.2018.12.0883
摘要 针对大数据聚类低效的问题,提出一种方形邻域快速网格密度聚类算法(square-neighborhood and grid-based DBSCAN,SGBSCAN)。首先给出方形邻域密度聚类定义,利用方形邻域代替圆形邻域,降低时间复杂度;其次提出方形邻域密度聚类的grid概念,快速确定高密度区域内核心点与数据点之间的密度关系;最后提出grid密度簇,利用网格之间的关系加快密度簇的形成。算法应用于16个数据集,分别与已有文献算法进行对比,结果表明所提算法在聚类效率方面有显著提升,数据量越大算法效率提升越明显,且该算法适用于多维数据的聚类。
关键词 聚类分析; 密度聚类; 方形邻域; 网格; 网格簇
基金项目 国家自然科学基金资助项目(61762046)
江西省自然科学基金资助项目(20161BAB212048)
本文URL http://www.arocmag.com/article/01-2020-06-026.html
英文标题 Grid density clustering algorithm based on square neighborhood
作者英文名 Lan Hong, Zhu Helong
机构英文名 School of Information Engineering,Jiangxi University of Science & Technology,Ganzhou Jiangxi 341400,China
英文摘要 To solve the problem of low efficiency of large data clustering, this paper proposed a fast grid density clustering algorithm SGBSCAN. Firstly, this paper gave the definition of square neighborhood density clustering, and used the square neighborhood instead of the circular neighborhood to reduce the time complexity. Secondly, this paper proposed the concept of grid of square neighborhood density clustering, and determined the density relationship between core points and data points in high density region quickly. Finally, this paper proposed the grid density cluster, and used the relationship between the grid to accelerate the formation of density clusters. It applied this algorithm to 16 data sets and compared with the existing literature algorithms. The results show that the algorithm has a significant improvement in clustering efficiency. The larger the data volume, the more obvious the efficiency of the algorithm, and the algorithm is suitable for multidimensional data clustering.
英文关键词 clustering analysis; density clustering; square neighborhood; grid; grid-based cluster
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收稿日期 2018/12/18
修回日期 2019/2/8
页码 1735-1740
中图分类号 TP274
文献标志码 A