| 引用本文: | 熊鹏文,周晓芸,熊宏锦,等.面向大规模交互数据空间划分的Voronoi图生成算法及应用.[J].国防科技大学学报,2022,44(1):129-136.[点击复制] |
| XIONG Pengwen,ZHOU Xiaoyun,XIONG Hongjin,et al.Voronoi diagram generation algorithm and application for large-scale interactive data space partition[J].Journal of National University of Defense Technology,2022,44(1):129-136[点击复制] |
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| 面向大规模交互数据空间划分的Voronoi图生成算法及应用 |
| 熊鹏文1,周晓芸1,熊宏锦2,张婷婷3 |
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(1. 南昌大学 信息工程学院, 江西 南昌 330031;2. 海装驻武汉地区军事代表局, 湖北 武汉 333000;3. 陆军工程大学 指挥控制工程学院, 江苏 南京 210007 )
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| 摘要: |
| 传统Voronoi图对大量点集进行Voronoi划分时会产生Voronoi单元格数过多的现象,导致难以适用于地理信息系统、生物医学等诸多领域。为了解决这个问题,提出一种自适应基于密度的聚类算法(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)的Voronoi图。阐述了Voronoi单元合并的现象,证明了其发生的充要条件,提出该Voronoi图的生成算法并进行仿真。通过显微镜下嗜中性粒细胞、我国地表火点数据对算法进行验证,结果表明,该算法能够有效解决点集规模较大时,Voronoi图划分过于细致的问题,突破了传统Voronoi图单点对单点的划分形式。此外,该算法拓宽了Voronoi图在图形图像处理、生物医学、地理信息系统等领域的应用。 |
| 关键词: Voronoi图 聚类 自适应参数 空间划分 |
| DOI：10.11887/j.cn.202201019 |
| 投稿日期：2020-08-07 |
| 基金项目:国家自然科学基金资助项目(62163024,61903175,61663027)；江西省主要学科学术和技术带头人项目(20204BCJ23006) |
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| Voronoi diagram generation algorithm and application for large-scale interactive data space partition |
| XIONG Pengwen1, ZHOU Xiaoyun1, XIONG Hongjin2, ZHANG Tingting3 |
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(1. School of Information Engineering, Nanchang University, Nanchang 330031, China;2. Wuhan Military Representatives Bureau of Naval Equipment Department, Wuhan 333000, China;3. Command and Control Engineering College, Army Engineering University, Nanjing 210007, China)
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| Abstract: |
| When the traditional Voronoi diagram divides a large number of point sets into Voronoi, there are too many Voronoi cells, which makes it difficult to apply to such fields as geographic information systems and biomedicine. In order to solve this problem, a Voronoi diagram based on adaptive DBSCAN (density-based spatial clustering of applications with noise) was proposed. The phenomenon of Voronoi unit merge was explained. The necessary and sufficient conditions for its occurrence were proved. The algorithm for generating the Voronoi diagram was proposed and simulated. In order to verify its effectiveness, the algorithm was applied to neutrophils under the microscope and fire point data on the surface of China. The results show that the algorithm can effectively solve the problem that Voronoi diagram is too meticulous when the point set size is large, which breaks through the single point to single point division form of the traditional Voronoi diagram. In addition,the algorithm broadens the application of Voronoi diagrams in the fields of graphic image processing,biomedicine and geographic information systems. |
| Keywords: Voronoi diagram clustering adaptive parameters space division |
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