期刊文献+

基于几何坐标法的多维数据可视化技术在地质数据处理中的应用 被引量:3

Geological application of multidimensional data visualization based on geometric coordinate method
下载PDF
导出
摘要 为简单且快速地查看地质研究过程中所获得地质数据属性信息的分布特征,用VC++6.0实现了常用的基于几何坐标法的多维数据可视化方法,即平行坐标法和圆形平行坐标法。这两个方法融合了直方图和等值线图各自的优点,可以同时查看数据的频数分布及其对应的坐标,还可以通过多个坐标轴显示数据,拓宽了一个图所能承载的数据信息,因此可以用这样的一个图形总体把握数据的分布特征。该方法在澳大利亚新南威尔士州Mandamah地区铜-金矿床钻孔数据的应用中,通过折线密度可以看出Au、Cu、Pb和Zn等元素都有高值突出,即高值附近折线密度稀疏,而大量数据聚集在低值附近,即低值附近折线分布密度高。 To display the distribution of attribute information in the geological research,in this paper,we have achieved the commonly used methods of multidimensional data visualization including parallel coordinates,circular parallel coordinates and the planar disperse points method with VC++6.0.Obviously,the parallel coordinate method and the circular parallel coordinates method both have the advantages of histogram and the contour map,so that,we can check the distribution of the frequency and its position at the same time.The data can be displayed in multi-coordinates using these methods,so there are more information to be born in one single map,then we can grasp the whole distribution features of the data using the map.These methods have been applied to display the drillhole data in the Cu-Au deposit in Mandamah,New South Wales,southeast of Australia.The density of the polygonal lines shows that there are high-value for the elements of Au,Cu,Pb,Zn,etc.,that is,there are spaase polygonal lines near the high-value,however,there are abundant data concentrated near the low values,i.e.,the polygonal lines are densely distributed near the low-value.
出处 《地学前缘》 EI CAS CSCD 北大核心 2012年第4期159-164,共6页 Earth Science Frontiers
基金 国家自然科学基金项目(41172302 40672196) 高等学校学科创新引智计划项目(B07011) 地质过程与矿产资源国家重点实验室项目
关键词 多维数据可视化 平行坐标法 圆形平行坐标法 multidimensional data visualization parallel coordinates circular parallel coordinates
  • 相关文献

参考文献10

  • 1申维,侯翠霞,房丛卉,边一.国际数学地球科学协会2009年会综述[J].地质通报,2010,29(1):165-168. 被引量:1
  • 2孙扬,封孝生,唐九阳,肖卫东.多维可视化技术综述[J].计算机科学,2008,35(11):1-7. 被引量:48
  • 3Chen C M. Top 10 unsolved information visualization prob- lems[J]. IEEE Computer Graphics and Applications, 2005, 25(4): 12-16.
  • 4Ghodsi A, Huang J, Schuurmans D. Local Tangent Space Em-bedding[R]. Technical Report, CS-2003 32. Waterloo: School of Computer Science, University of Waterloo, 2003.
  • 5Quist M, Yona G. Distributional scaling: An algorithm for structure-preserving embedding of metric and nonmetric spaces[J]. Journal of Machine Learning Research, 2004(5): 399-420.
  • 6Fanea E. An interactive 3D integration of parallel coordinates and Star Glyphs[C]//IEEE Symposium on Information Visu- alization. Minneapolis, MN, 2005: 149-156.
  • 7Grinstein G G, Hoffman P E, Laskowski S J, et al. Bench- mark development for the evaluation of visualization for data mining[M]//Information Visualization in Data Mining and Knowledge Discovery. San Francisco, CA:Morgan Kauf- mann Publishers Inc, 2001: 129-176.
  • 8Hoffman P E. Table Visualizations: A Formal Model and its Applications[D]. Lowell, MA: Computer Science Depart- ment, University of Massachusetts, 1999.
  • 9Novotny M, Hauser H. Outlier-preserving focus+ context visualization in parallel coordinates[J]. IEEE Transactions on Visualization and Computer Graphics, 2006, 12 (5) : 893-900.
  • 10Cohen D R, Kelley D L, Anand R. et al. Major advances in exploration geochemistry 1998-2007[J]. Geochemistry: Ex- ploration, Environment, Analysis, 2010, 10(1):3-16.

二级参考文献47

  • 1宋枫溪,高秀梅,刘树海,杨静宇.统计模式识别中的维数削减与低损降维[J].计算机学报,2005,28(11):1915-1922. 被引量:44
  • 2邵超,黄厚宽.一种新的基于SOM的数据可视化算法[J].计算机研究与发展,2006,43(3):429-435. 被引量:9
  • 3汪加才,张金城,江效尧.一种有效的可视化孤立点发现与预测新途径[J].计算机科学,2007,34(6):200-203. 被引量:5
  • 4Chen Chaomei. Top 10 unsolved information visualization prob lems. IEEE Computer Graphics and Applications, July/August 2005:12-16
  • 5Jain A K, Murty M N, Flynn P J. Data clustering: a review. ACM Computing Surveys, 1999,31 (3) : 264-323
  • 6Santos S D , Brodlie K. Gaining understanding of multivariate and multidimensional data through visualization. Computers&Graphies,2004,28(1) : 311-325
  • 7Wong P C, Bergeron R D. 30 Years of Multidimensional Multivariate Visualization // Scientific Visualization-Overviews, Methodologies, and Techniques. Washington, DC: IEEE Computer Society Press, 1997:3 34
  • 8Keim D A, Ankerst M. Visual data mining and exploration of large databases. PKDD. Freiburg, Germany, 2001
  • 9Inselberg A. The Plane with Parallel Coordinates. The Visual Computer, 1985,1(2): 69-91
  • 10Hoffman P E. Table Visualizations : A Formal Model and its Applications. Doctoral Diss. , Lowell, MA: Computer Science Dept. ,University of Massachusetts, 1999

共引文献47

同被引文献7

引证文献3

二级引证文献18

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部