基于方形邻域的网格密度聚类算法  

Grid density clustering algorithm based on square neighborhood

作　　者：兰红[1] 朱合隆 Lan Hong;Zhu Helong(School of Information Engineering,Jiangxi University of Science&Technology,Ganzhou Jiangxi 341400,China)

机构地区：[1]江西理工大学信息工程学院,江西赣州341400

出　　处：《计算机应用研究》2020年第6期1735-1740,共6页Application Research of Computers

基　　金：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)。

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 gridbased 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。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.

关 键 词：聚类分析 密度聚类 方形邻域 网格 网格簇 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]