伏家云,靖常峰,杜明义,付艳丽,戴培培

• 1:北京建筑大学测绘与城市空间信息学院
• 2:现代城市测绘国家测绘地理信息局重点实验室
• 3:北京帝测科技股份有限公司

摘要(Abstract)：

针对DBSCAN算法参数对聚类结果具有较大的不确定性问题,该文提出了基于空间分析的参数优化思想:首先,基于Ripley’s K函数分析,实现自适应确定数据聚类范围EPS值;基于K-D树分析,实现自适应确定在Eps阈值内的点数量MinPts值;然后,基于以上参数的自适应确定思想,利用R语言编写了DBSCAN算法,进一步实现了数据的精确聚类。基于典型城市管理案件的实验结果表明:该方法充分考虑了空间数据统计特性,具有较好的适用性,聚类簇特征明显,聚类质量较高。

关键词(KeyWords)： DBSCAN算法;城管案件;聚类分析;数据挖掘

Abstract:

Keywords:

基金项目(Foundation): 国家测绘地理信息局现代城市测绘重点实验室开放基金项目(20141204NY);; 北京建筑大学科研基金项目(ZF15071);北京建筑大学教育科研项目(Y1501);北京建筑大学研究生创新资助项目(PG2017017);; 城市空间信息工程北京市重点实验室经费资助项目(2016203)

作者(Author): 伏家云,靖常峰,杜明义,付艳丽,戴培培

DOI: 10.16251/j.cnki.1009-2307.2018.08.022

参考文献(References)：

• [1]吉根林,赵斌.面向大数据的时空数据挖掘综述[J].南京师大学报,2014,37(1):1-7.(JI Genlin,ZHAO Bin.A survey of spatiotemporal data mining for big data[J].Journal of Nanjing Normal University,2014,37(1):1-7.)
• [2]熊竟.大数据时代的理念创新与城市精细化管理[J].上海城市管理,2014,23(4):23-26.(XIONG Jing.Conceptual innovation in big data times and refined treatment of cities[J].Shanghai Urban Management,2014,23(4):23-26.)
• [3]李新延,李德仁.DBSCAN空间聚类算法及其在城市规划中的应用[J].测绘科学,2005,30(3):51-53.(LI Xinyan,LI Deren.DBSCAN spatial clustering algorithm and its application in urban planning[J].Science of Surveying and Mapping,2005,30(3):51-53.)
• [4]任兴平,何忠龙,孟增辉.改进DBSCAN算法中参数Eps值的确定[J].现代电子术,2007(11):120-121.(REN Xingping,HE Zhonglong,MENG Zenghui.The improved algorithm of decide the parameter Eps in DBSCAN[J].Modern Electronic Technique,2007(11):120-121.)
• [5]周董,刘鹏.VDBSCAN:变密度聚类算法[J].计算机工程与应用,2009,45(11):137-141.(ZHOU Dong,LIU Peng.VDBSCAN:varied density based clustering algorithm[J].Computer Engineering and Applications,2009,45(11):137-141.)
• [6]夏鲁宁,荆继武.SA-DBSCAN:一种自适应基于密度聚类算法[J].中国科学院研究生院学报,2009,26(4):530-538.(XIA Luning,JING Jiwu.SA-DBSCAN:a self-adaptive density-based clustering algorithm[J].Journal of the Graduate School of the Chinese Academy of Sciences,2009,26(4):530-538.)
• [7]李宗林,罗可.DBSCAN算法中参数的自适应确定[J].计算机工程与应用,2016,52(3):70-73,80.(LI Zonglin,LUO Ke.Research on adaptive parameters determination in DBSCAN algorithm[J].Computer Engineering and Applications,2016,52(3):70-73,80.)
• [8]STEFANAKIS E.NET-DBSCAN:clustering the nodes of a dynamic linear network[J].International Journal of Geographical Information Science,2007,21(4):427-442.
• [9]PANDEY A K,DUBEY R.A survey on study of enhanced partition based DBSCAN algorithm[J].International Journal of Management,IT and Engineering,2014,4(3):115-123.
• [10]黄钢,吴超仲,吕能超.基于改进DBSCAN算法的激光雷达目标物检测方法[J].交通信息安全,2015,33(3):23-28.(HUANG Gang,WU Chaozhong,LNengchao.A study of laser radar object detection based on improved DBSCAN algorithm[J].Journal of Transport Information and Safety,2015,33(3):23-28.)
• [11]TANG W W,FENG W P,JIA M J.Massively parallel spatial point pattern analysis:Ripley’s K function accelerated using graphics processing units[J].International Journal of Geographical Information Science,2015,29(3):412-439.
• [12]张一,江刚武,狄亚南,等.一种采用改进K-d树的无人机影像特征匹配搜索方法[J].测绘科学技术学报,2015,32(5):500-504.(ZHANG Yi,JIANG Gangwu,DI Yanan,et al.An optimized K-d tree search method for feature matching of UAV images[J].Journal of Geomatics Science and Technology,2015,32(5):500-504.)
• [13]董林,舒红,牛宵.利用叠置分析和面积计算实现空间关联规则挖掘[J].武汉大学学报(信息科学版),2013,38(1):95-99.(DONG Lin,SHU Hong,NIU Xiao.Spatial association rule mining based on overlay analysis and area calculation[J].Geomatics and Information Science of Wuhan University,2013,38(1):95-99.)
• [14]张忠贵.市政公用基础设施时空信息集成管理技术研究[D].武汉:中国地质大学,2014.(ZHANG Zhonggui.Research on integrated management technology of municipal infrastructure spatiotemporal information[D].Wuhan:China University of Geosciences,2014.)
• [15]肖宇鹏.空间聚类分析的研究[D].哈尔滨:哈尔滨理工大学,2015.(XIAO Yupeng.Research on the spatial clustering analysis[D].Harbin:Harbin University of Science and Technology,2015.)