基于区间分类的螺旋图可视化边绑定方法
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摘要
在时序数据可视化领域,螺旋图是一种常用的可视化方法,它既能将多个阶段的数据同时展示在一个平面空间内,又能在有限的空间内展示任意时长的数据。针对现有的螺旋图可视化方法在展示大量的时间序列数据时会出现因螺旋线交叉而导致视觉杂乱的问题,研究螺旋图可视化方法意义非凡。首先将状态圆环上的数据点进行分类;然后在相邻的状态圆环之间设置虚拟绑定圆环,通过边绑定的函数将状态圆环上的数据点映射到其对应的虚拟绑定圆环上;最后在状态圆环与其对应的虚拟绑定圆环之间绘制Bézier曲线,在虚拟绑定圆环与虚拟绑定圆环之间绘制螺旋线,从而实现边绑定的效果。实验结果表明,该边绑定算法能够有效地对大规模数据进行可视化,并能有效地缓解视觉杂乱的问题。
Spiral graph is a common visualization method in visualizing time series data.It can not only simultaneous display the multiple-stages data in one plane space,but also demonstrate the data with different time length in a limited space.In order to solve the problem of visual clutter caused by the intersection of helical lines in the present spiral image visualization methods,a method of edge bundling is of great significance.First,the data points on the state circle are classified.Then the virtual bundling circles are set between the adjacent state circles,and the data points on the state ring are mapped to the corresponding virtual bundling circle by the function of edge bundling.Finally,in order to achieve the effect of curve bundling,the Bézier curve is drawn between the state circle and its corresponding virtual bundling circle,and the spiral curve is drawn between the virtual bundling circle and the virtual bundling circle.Experimental results show that the edge-bundling algorithm is effective for large-scale data visualization and can effectively alleviate the problem of visual clutter.
Spiral graph is a common visualization method in visualizing time series data.It can not only simultaneous display the multiple-stages data in one plane space,but also demonstrate the data with different time length in a limited space.In order to solve the problem of visual clutter caused by the intersection of helical lines in the present spiral image visualization methods,a method of edge bundling is of great significance.First,the data points on the state circle are classified.Then the virtual bundling circles are set between the adjacent state circles,and the data points on the state ring are mapped to the corresponding virtual bundling circle by the function of edge bundling.Finally,in order to achieve the effect of curve bundling,the Bézier curve is drawn between the state circle and its corresponding virtual bundling circle,and the spiral curve is drawn between the virtual bundling circle and the virtual bundling circle.Experimental results show that the edge-bundling algorithm is effective for large-scale data visualization and can effectively alleviate the problem of visual clutter.
引文
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[12]PALMAS G,BACHYNSKYI M,OULASVIRTA A,et al.An edge-bundling layout for interactive parallel coordinates[C]∥Proceedings of IEEE Pacific Visualization Symposium.IEEE,2014:57-64.
[13]SPURR B D.Density estimation for statistics and data analysis[J].Journal of the Royal Statistical Society,1987,150(4):403-404.
[14]QIN H X,WEI X S.A Study on Edge Bundling Technology in Parallel Coordinates[J].Journal of Computer-Aided Design&Computer Graphics,2017,29(7):1235-1244.(in Chinese)秦红星,卫学仕.平行坐标中的边捆绑算法[J].计算机辅助设计与图形学学报,2017,29(7):1235-1244.
[15]KANUNGO T,MOUNT D M,NETANYAHU N S,et al.An efficient k-means clustering algorithm:analysis and implementation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):881-892.
[16]HOLTEN D,VAN WIJK J J.Evaluation of Cluster Identification Performance for Different PCP Variants[J].Computer Graphics Forum,2010,29(3):793-802.
1)http://www.12306.cn/mormhweb/
[2]TOMINSKI C,ABELLO J,SCHUMANN H.Axes-based visualizations with radial layouts[C]∥Proceedings of ACM Symposium on Applied Computing.ACM,2004:1242-1247.
[3]LEI H,XIA J,GUO F,et al.Visual exploration of latent ranking evolutions in time series[J].Journal of Visualization,2016,19(4):1-13.
[4]JIANG T T,XIAO W D,ZHANG C,et al.Text visualization method for time series based on Sankey diagram[J].Application Research of Computers,2016,33(9):2683-2687.(in Chinese)姜婷婷,肖卫东,张翀,等.基于桑基图的时间序列文本可视化方法[J].计算机应用研究,2016,33(9):2683-2687.
[5]BOUALI F,DEVAUX S,BASTIEN,et al.Visual mining of time series using a tubular visualization[J].Visual Computer,2016,32(1):15-30.
[6]YANG H H,LI T R,CHEN X D.Visualization of time series data based on spiral graph[J].Journal of Computer Applications,2017,37(9):2443-2448.(in Chinese)杨欢欢,李天瑞,陈馨菂.基于螺旋图的时间序列数据可视化[J].计算机应用,2017,37(9):2443-2448.
[7]ZHOU H,XU P,YUAN X,et al.Edge Bundling in Information Visualization[J].Tsinghua Science and Technology,2013,18(2):145-156.
[8]GANSNER E,HU Y,NORTH S,et al.Multilevel agglomerative edge bundling for visualizing large graphs[C]∥Proceedings of IEEE Pacific Visualization Symposium.IEEE,2011:187-194.
[9]MCDONNELL K T,MUELLER K.Illustrative parallel coordinates[J].Computer Graphics Form,2008,27(3):1031-1038.
[10]ZHOU H,YUAN X R,QU H M,et al.Visual clustering in parallel coordinates[J].Computer Graphics Forum,2008,27(3):1047-1054.
[11]HEINRICH J,LUO Y,KIRKPATRICK A E,et al.Evaluation of a bundling technique for parallel coordinates[J].Energy Conversion&Management,2011,88(5):259-266.
[12]PALMAS G,BACHYNSKYI M,OULASVIRTA A,et al.An edge-bundling layout for interactive parallel coordinates[C]∥Proceedings of IEEE Pacific Visualization Symposium.IEEE,2014:57-64.
[13]SPURR B D.Density estimation for statistics and data analysis[J].Journal of the Royal Statistical Society,1987,150(4):403-404.
[14]QIN H X,WEI X S.A Study on Edge Bundling Technology in Parallel Coordinates[J].Journal of Computer-Aided Design&Computer Graphics,2017,29(7):1235-1244.(in Chinese)秦红星,卫学仕.平行坐标中的边捆绑算法[J].计算机辅助设计与图形学学报,2017,29(7):1235-1244.
[15]KANUNGO T,MOUNT D M,NETANYAHU N S,et al.An efficient k-means clustering algorithm:analysis and implementation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):881-892.
[16]HOLTEN D,VAN WIJK J J.Evaluation of Cluster Identification Performance for Different PCP Variants[J].Computer Graphics Forum,2010,29(3):793-802.
1)http://www.12306.cn/mormhweb/