Computer Science ›› 2018, Vol. 45 ›› Issue (12): 262-267.doi: 10.11896/j.issn.1002-137X.2018.12.043

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

Visual Analysis Method of Garbage Disposal Modes Based on Self-organizing Maps Clustering

QIN Xu-jia, SHAN Yang-yang, XU Fei, ZHENG Hong-bo, ZHANG Mei-yu   

  1. (School of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China)
  • Received:2017-06-27 Online:2018-12-15 Published:2019-02-25

Abstract: A hybrid visual analysis method was proposed for the data of garbage disposal modes in various provinces of China.In order to analyze the data from multiple views,this paper combined three visualization techniques including unified distance matrix,parallel coordinate and Small-Multiple,designed and realized the interactions among the three views.Firstly,the data are processed by clustering so that the garbage disposal modes of different provinces in recent years can be divided into different categories.The clustering used in this paper is based on self-organizing maps (SOM) neural network clustering algorithm.Secondly,according to the results of SOM clustering,the unified distance matrix is used for visualization,and the parallel coordinate is used to describe the attributes of each clustering result.In order to analyze the geographical and sequential attributes of data,the Small-Multiple visualization technology is used.Thirdly,interactions such as multi-view linkage and refreshing are implemented to help users explore the data on their own and achieve interactive display and analysis of multiple views.The experimental results show that the hybrid visualization method can achieve a good visualization effect of multi-attributes interaction and can help users to understand the distribution and trend analysis of garbage disposal modes in China.

Key words: Parallel coordinate, Small-Multiple, SOM clustering, Unified distance matrix, Visualization

CLC Number: 

  • TP391
[1]TUFTE E R.Beautiful Evidence[M].Cheshire:Graphics Press,2006.
[2]REIJNER H.The development of the horizon graph[C]∥Proceedings of IEEE Visualization Workshop From Theory to Practice:Design,Vison and Visualization.2008.
[3]TUFTE E R.Envisioning information[J].Optometry & Vision Science,1991,68(4):322-324.
[4]WEBER M,ALEXA M,MÜLLER W.Visualizing Time-Serieson Spirals[C]∥Proceedings of the IEEE Symposium on Information Visualization 2001 (InfoVis01).2001:7-14.
[5]VAN WIJK J J,VAN SELOW E R.Cluster and calendar based visualization of time series data[C]∥Proceedings of the IEEE Symposium on Information Visualization 1999(InfoVis’99).1999:4-9.
[6]GOODWIN S,DYKES J,SLINGSBY A,et al.Visualizing multiple variables across scale and geography[J].IEEE Transactions on Visualization and Computer Graphics,2016,22(1):599-608.
[7]HEILMANN R,KEIM D A,PANSE C,et al.Recmap:Rectangular map approximations[C]∥Proceedings of the IEEE Symposium on Information Visualization 2004(InfoVis’04).2004:33-40.
[8]KEIM D A,PANSE C,NORTH S C.Medial-axis-based cartograms[J].IEEE Computer Graphics and Applications,2005,25(3):60-68.
[9]COLLINS C,PENN G,CARPENDALE S.Bubble sets:Revealing set relations with isocontours over existing visualizations[J].IEEE Transactions on Visualization and Computer Graphi-cs,2009,15(6):1009-1016.
[10]QU H,CHAN W Y,XU A,et al.Visual analysis of the air pollution problem in Hong Kong[J].IEEE Transactions on Visuali-zation and Computer Graphics,2007,13(6):1408-1415.
[11]IM J F,MCGUFFIN M J,LEUNG R.GPLOM:the generalized plot matrix for visualizing multidimensional multivariate data[J].IEEE Transactions on Visualization and Computer Graphi-cs,2013,19(12):2606-2614.
[12]GUO D,CHEN J,MACEACHREN A M,et al.A visualization system for space-time and multivariate patterns (vis-stamp)[J].IEEE Transactions on Visualization and Computer Graphics,2006,12(6):1461-1474.
[13]PENG W,WARD M O,RUNDENSTEINER E A.Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering[C]∥Proceedings of the IEEE Symposium on Information Visualization 2004(InfoVis’04).2004:89-96.
[14]WARD M,GRINSTEIN G,KEIM D.Interactive Data Visualization:Foundations,Techniques,and Applications[J].Seed & Plant Production Journal,2010,51(4):188-195.
[15]WARD M O.Xmdvtool:Integrating multiple methods for visuali-zing multivariate data[C]∥Proceedings of the Conference on Visualization’94.IEEE Computer Society Press,1994:326-333.
[16]国家统计局.中国统计年鉴[M].北京:中国统计出版社,2016.
[1] YANG Xiao, WANG Xiang-kun, HU Hao, ZHU Min. Survey on Visualization Technology for Equipment Condition Monitoring [J]. Computer Science, 2022, 49(7): 89-99.
[2] CHEN Hui-pin, WANG Kun, YANG Heng, ZHENG Zhi-jie. Visual Analysis of Multiple Probability Features of Bluetongue Virus Genome Sequence [J]. Computer Science, 2022, 49(6A): 27-31.
[3] ZHU Min, LIANG Zhao-hui, YAO Lin, WANG Xiang-kun, CAO Meng-qi. Survey of Visualization Methods on Academic Citation Information [J]. Computer Science, 2022, 49(4): 88-99.
[4] LI Jia-zhen, JI Qing-ge, ZHU Yong-lin. Ray Tracing Checkerboard Rendering in Molecular Visualization [J]. Computer Science, 2022, 49(2): 134-141.
[5] LI Jia-zhen, JI Qing-ge. Dynamic Low-sampling Ambient Occlusion Real-time Ray Tracing for Molecular Rendering [J]. Computer Science, 2022, 49(1): 175-180.
[6] LUO Jing-jing, TANG Wei-zhen, DING Ji-ting. Research of ATC Simulator Training Values Independence Based on Pearson Correlation Coefficient and Study of Data Visualization Based on Factor Analysis [J]. Computer Science, 2021, 48(6A): 623-628.
[7] SU Qing, LI Zhi-zhou, LIU Tian-tian, WU Wei-min, HUANG Jian-feng, LI Xiao-mei. Tree Structure Evaluation Visualization Model for Program Debugging [J]. Computer Science, 2021, 48(5): 68-74.
[8] E Hai-hong, ZHANG Tian-yu, SONG Mei-na. Web-based Data Visualization Chart Rendering Optimization Method [J]. Computer Science, 2021, 48(3): 119-123.
[9] ZHANG Qian, XIAO Li. Review of Visualization Drawing Methods of Flow Field Based on Streamlines [J]. Computer Science, 2021, 48(12): 1-7.
[10] MA Meng-yu, WU Ye, CHEN Luo, WU Jiang-jiang, LI Jun, JING Ning. Display-oriented Data Visualization Technique for Large-scale Geographic Vector Data [J]. Computer Science, 2020, 47(9): 117-122.
[11] LV Ze-yu, LI Ji-xuan, CHEN Ru-Jian and CHEN Dong-ming. Research on Prediction of Re-shopping Behavior of E-commerce Customers [J]. Computer Science, 2020, 47(6A): 424-428.
[12] LI Tian-pei, CHEN Li. Retinal Vessel Segmentation Based on Dual Attention and Encoder-decoder Structure [J]. Computer Science, 2020, 47(5): 166-171.
[13] SHANG Jun-yuan, YANG Le-han, HE Kun. Analyzing Latent Representation of Deep Neural Networks Based on Feature Visualization [J]. Computer Science, 2020, 47(5): 190-197.
[14] DU Liu-yun, ZHENG Zhi-jie, ZHENG Hua-xian. Visualization of DNA Sequences of Two Kinds of Bacteria Under Firmicutes [J]. Computer Science, 2020, 47(11A): 192-195.
[15] WANG Yang, LI Peng, JI Yi-mu, FAN Wei-bei, ZHANG Yu-jie, WANG Ru-chuan, CHEN Guo-liang. High Performance Computing and Astronomical Data:A Survey [J]. Computer Science, 2020, 47(1): 1-6.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!