基于聚类的海量空间数据可视化研究与应用
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摘要
随着空间数据的爆炸性增长,空间数据可视化已经成为处理空间信息的重要方法和关键技术。论文首先简要陈述了当前空间数据可视化出现的问题,并对相应的方法进行了归纳及分析。在此基础上,提出了一种基于聚类的空间数据可视化方法,先通过聚类算法对空间数据进行聚类分析,然后将得到的结果进行可视化,从而解决了原有方法造成的数据拥堵、重复叠加现象。最后,对论文相关工作进行了总结以及对海量空间数据可视化的发展进行了展望。
AbsrtactWith the explosive growth of spatial data,spatial data visualization has become an important method and key tech-nology for spatial information processing. This paper first introduces the existing problems of visualization of spatial data,and analyz-es and summarizes the methods of spatial data visualization. On this basis,a kind of spatial data visualization method based on clus-tering of spatial data clustering analysis is put forward by clustering algorithm,then the results are visualized,so as to solve theproblem caused by the original data visualization method of congestion,overlap. Finally,the related work of this paper is summa-rized,and the development of visualization of mass spatial data is prospected.
AbsrtactWith the explosive growth of spatial data,spatial data visualization has become an important method and key tech-nology for spatial information processing. This paper first introduces the existing problems of visualization of spatial data,and analyz-es and summarizes the methods of spatial data visualization. On this basis,a kind of spatial data visualization method based on clus-tering of spatial data clustering analysis is put forward by clustering algorithm,then the results are visualized,so as to solve theproblem caused by the original data visualization method of congestion,overlap. Finally,the related work of this paper is summa-rized,and the development of visualization of mass spatial data is prospected.
引文
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[17]王瑞松.大数据环境下时空多维数据可视化研究[D].杭州:浙江大学,2016.WANG Ruisong.Spatio temporal multidimensional data visualization in big data environment[D].Hangzhou:Zhejiang University,2016.
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[2]童庆,张敬谊,陈诚.基于GIS系统的可视化应急方案研究[J].计算机工程与应用,2011,47(20):4-8.TONG Qing,ZHANG Jingyi,CHEN Cheng.Research on Visualization emergency plan based on GIS system[J].Computer engineering and application,2011,47(20):4-8.
[3]屈华民.大数据时代的可视化与协同创新[J].新美术,2013(11):21-27.QU Huamin.Visualization and collaborative innovation in the era of big data[J].New fine arts,2013(11):21-27.
[4]黄金花.聚类算法的分析与比较[J].科技信息:科学·教研,2008(13):254-254.HUANG Jinhua.Analysis and comparison of clustering algorithms[J].Science and technology information:Science,teaching and research,2008(13):254-254.
[5]杨天霞.基于序列模式的序列聚类挖掘算法研究[D].兰州:西北师范大学,2010.YANG Tianxia.Sequential clustering algorithm based on sequential patterns[D].Lanzhou:Northwest Normal University,2010.
[6]李凯,李昆仑,崔丽娟.模型聚类及在集成学习中的应用研究[C]//中国分类技术及应用学术会议,2007:203-207.LI Kai,LI Kunlun,CUI Lijuan.Model clustering and its application in ensemble learning[C]//Chinese classification technology and application academic conference,2007:203-207.
[7]王军.基于网格密度聚类的雷达信号分选算法研究[D].镇江:江苏科技大学,2013.WANG Jun.Research on radar signal sorting algorithm based on grid density clustering[D].Zhenjiang:Jiangsu University of Science and Technology,2013.
[8]齐坡.空间方向关系的关键技术研究[D].哈尔滨:哈尔滨理工大学,2014.QI Po.Research on Key Technologies of spatial directional relations[D].Harbin:Harbin University of Science and Technology,2014.
[9]张洋,王辰.基于聚类的空间数据可视化方法[J].计算机应用,2013,33(10):2981-2983.ZHANG Yang,WANG Chen.Visualization method of spatial data based on clustering[J].Computer application,2013,33(10):2981-2983.
[10]Jain A K.Data Clustering:50 Years Beyond K-means[M].Machine Learning and Knowledge Discovery in Databases.Springer Berlin Heidelberg,2008:651-666.
[11]Patwary M M A,Liao W,Manne F,et al.A new scalable parallel DBSCAN algorithm using the disjoint-set data structure[J].2012:1-11.
[12]Alex Rodriguez,Alessandro Laio.Machine learning.Clustering by fast search and find of density peaks[J].Science,2014,344(6191):1492.
[13]Wang S,Wang D,Caoyuan L I,et al.Clustering by Fast Search and Find of Density Peaks with Data Field[J].Chinese Journal of Electronics,2016,25(3):397-402.
[14]Mehmood R,Bie R,Dawood H,et al.Fuzzy Clustering by Fast Search and Find of Density Peaks[C]//International Conference on Identification,Information,and Knowledge in the Internet of Things.IEEE,2016:258-261.
[15]Andrienko G,Andrienko N,Rinzivillo S,et al.Interactive visual clustering of large collections of trajectories[C]//Visual Analytics Science and Technology,2009.VAST 2009.IEEE Symposium on.IEEE,2009:3-10.
[16]蒋礼青,张明新,郑金龙,等.快速搜索与发现密度峰值聚类算法的优化研究[J].计算机应用研究,2016,33(11):3251-3254.JIANG Liqing,ZHANG Mingxin,ZHENG Jinlong,et al.Optimization Research of fast search and discovery density peak clustering algorithm[J].computer application research,2016,33(11):3251-3254.
[17]王瑞松.大数据环境下时空多维数据可视化研究[D].杭州:浙江大学,2016.WANG Ruisong.Spatio temporal multidimensional data visualization in big data environment[D].Hangzhou:Zhejiang University,2016.
[18]Michael B,Vadim O,Jeffrey H.D(3):Data-Driven Documents[J].IEEE Transactions on Visualization&Computer Graphics,2011,17(12):2301-9.