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Anisotropic parallel coordinates with adjustment based on distribution features

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Abstract

To measure the confidence degree of correlation between data dimensions in multidimensional data, we present a visualization method named anisotropic parallel coordinates. The method introduces distribution features of data into classical parallel coordinates scheme. The method first divides the data in each dimension into segments and obtains the frequency of data in each segment. The histogram is adopted to express the distribution of data in each dimension. The coordinate axis of each dimension is adjusted according to the corresponding distribution features. The principle of the adjustment is to amplify the occupation in the axis for the data segment with biggish frequency, while compacting the segment with lesser frequency. The adjustment can improve the capability of expressing the correlativity between the adjacent dimensions effectively in the final visualization result. The experimental results prove the method presented in the paper can achieve more effective expression to the correlativity between the adjacent dimension data. The improved effect can enhance the efficiency of the visual interaction and the visual analysis for the multidimensional data.

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References

  • Alexander L, Christian P, Karl K (2010) Comparative analysis of multidimensional, quantitative data. IEEE Trans Vis Comput Graph 16(6):1027–1035. doi:10.1109/TVCG.2010.138

    Article  Google Scholar 

  • Aritra D, Kosara R (2010) Pargnostics: screen-space metrics for parallel coordinates. IEEE Trans Vis Comput Graph 16(6):1017–1026. doi:10.1109/TVCG.2010.184

    Article  Google Scholar 

  • Artero AO, Oliveira MCF, Levkowitz H (2004) Uncovering clusters in crowded parallel coordinates visualizations. In Proceedings of IEEE information visualization, pp 81–88. doi:10.1109/INFVIS.2004.68

  • Baldonado MQW, Woodruff A, Kuchinsky A (2000) Guidelines for using multiple views in information visualization. In Proceedings of AVI’00, ACM, pp. 110–119. doi:10.1145/345513.345271

  • Chad AS, Daniel MR, Galen S (2013) Big data visual analytics for exploratory earth system simulation analysis. Comput Geosci 61(71–82):2013. doi:10.1016/j.cageo.2013.07.025

    Google Scholar 

  • Chen Y, Cheng XC, Chen HQ (2011) A multidimensional data visualization method based on parallel coordinates and enhanced ring. In: International conference on computer science and network technology vol 4, pp 2224–2229. doi: 10.1109/ICCSNT.2011.6182418

  • Ellis G, Dix A (2006) Enabling automatic clutter reduction in parallel coordinate plots. IEEE Trans Vis Comput Graph 12(5):717–724. doi:10.1109/TVCG.2006.138

    Article  Google Scholar 

  • Elmqvist N, Dragicevic P, Fekete JD (2008) Rolling the dice: multi-dimensional visual exploration using scatter plot matrix navigation. IEEE Trans Vis Comput Graph 14(6):1141–1148. doi:10.1109/TVCG.2008.153

    Article  Google Scholar 

  • Fanea E, Carpendale S, Isenberg T (2005) An interactive 3d integration of parallel coordinates and star glyphs. In: Proceedings of the IEEE Info-Vis’05, pp 149–156. doi:10.1109/INFVIS.2005.1532141

  • Geng Z, Peng Z, Laramee RS, Roberts JC, Walker R (2011) Angular histograms: frequency-based visualizations for large, high dimensional data. IEEE Trans Vis Comput Graph 17(12):2572–2580. doi:10.1109/TVCG.2011.166

    Article  Google Scholar 

  • Gregorio P, Bachynskyi M, Oulasvirta A et al (2014) An edge-bundling layout for interactive parallel coordinates. Proc IEEE Pacific Vis 57–64(2014):2014. doi:10.1109/PacificVis.40

    Google Scholar 

  • Hauser H, Ledermann F, Doleisch H (2002) Angular brushing of extended parallel coordinates. In: Proceedings of the IEEE symposium on information visualization 2002 (InfoVis’02), pp 127–130. doi:10.1109/INFVIS.2002.1173157

  • Heinrich J, Weiskopf D (2013) State of the art of parallel coordinates. STAR Proc Euro-Graph 2013:95–116. doi:10.2312/conf/EG2013/stars/095-116

    Google Scholar 

  • Inselberg A (1985) The plane with parallel coordinates. Vis Comput 1(2):69–91. doi:10.1007/BF01898350

    Article  MathSciNet  MATH  Google Scholar 

  • Inselberg A, Dimsdale B (1990) Parallel coordinates: a tool for visualizing multi-dimensional geometry. Proc IEEE Vis 90(361–378):1990

    Google Scholar 

  • Ivosev G, Burton L, Bonner R (2008) Dimensionality reduction and visualization in principal component analysis. Anal Chem 80(13):4933–4944. doi:10.1021/ac800110w

    Article  Google Scholar 

  • McDonnell KT, Mueller K (2008) Illustrative parallel coordinates. In: Computer graphics forum. Blackwell Publishing Ltd, vol 27, pp 1031–1038

  • Neumann P, Tang A, Carpendale S (2007) A framework for visual information analysis. Technical report 2007–87123, University of Calgary, Calgary, AB, Canada

  • Novotny M, Hauser H (2006) Outlier-preserving focus + context visualization in parallel coordinates. IEEE Trans Vis Comput Graph 12(5):893–900. doi:10.1109/TVCG.2006.170

    Article  Google Scholar 

  • Ren L (2009) A research for interaction techniques in information visualization. The Chinese Academy of Sciences

  • Ren YG, Yu G (2005) Clustering for multi-dimensional data and its visualization. Chin J Comput 28(11):1861–1865. doi:10.3321/j.issn:0254-4164.2005.11.012

    Google Scholar 

  • Ruebel O, Wu K, Childs H, et al (2008) High performance multivariate visual data exploration for extremely large data. In: Proceedings of the 2008 ACM/IEEE conference on supercomputing. IEEE Press, 2008: Article No. 51: pp 1–12

  • Shaik JS, Yeasin M (2006) Visualization of high dimensional data using an automated 3d star coordinate system. In: International joint conference on neural networks sheraton vancouver wall centre hotel. Vancouver, BC, Canada. doi:10.1109/IJCNN.2006.246848

  • Sun Y, Tang JY, Tang DQ, Xiao WD (2010) Improved multivariate data visualization method. Chin J Softw 21(6):1462–1472. doi:10.3724/SP.J.1001.2010.03460

    Article  Google Scholar 

  • Wang WX, Dai GZ (2006) Inforrmation visualization. Adv Inform Sci 6:80–118 (in Chinese)

    Google Scholar 

  • Wang B, Ruchikachorn P, Mueller K (2013) SketchPadN-D: WYDIWYG sculpting and editing in high-dimensional space. IEEE Trans Vis Comput Graph 19(12):2060–2069

    Article  Google Scholar 

  • Yang J, Peng W, Ward MO, et al (2003) Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets. In: IEEE symposium on information visualization. Seattle, Washington. doi:10.1109/INFVIS.2003.1249015

  • Yuan XR, Nguyen Minh X, Chen BQ et al (2006) HDR VolVis: high dynamic range volume visualization. IEEE Trans Vis Comput Graph 12(4):433–445

    Article  Google Scholar 

  • Yuan X, Guo P, Xiao H et al (2009) Scattering points in parallel coordinates. IEEE Trans Vis Comput Graph 15(6):1001–1008. doi:10.1109/TVCG.2009.179

    Article  Google Scholar 

  • Zhao G, Peng ZM, Robert S et al (2011) Angular histograms: frequency-based visualizations for large high dimensional data. IEEE Trans Vis Comput Graph 17(12):2572–2580. doi:10.1109/TVCG.2011.166

    Article  Google Scholar 

  • Zhou H, Yuan X, Qu H et al (2008) Visual clustering in parallel coordinates. Comput Graph Forum 27(3):1047–1054. doi:10.1111/j.1467-8659.2008.01241.x

    Article  Google Scholar 

  • Zhou H, Cui W, Qu H et al (2009) Splatting the lines in parallel coordinates. Comput Graph Forum 28(3):759–766. doi:10.1111/j.1467-8659.2009.01476.x

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the Twelfth Five-Year National Science and Technology Support Project (2012BAD29B01-2), Beijing Natural Science Foundation (4154066), the Science and Technology Plan Project of Beijing Municipal Commission of Education (PXM2014-014213-000004), Beijing Outstanding Personnel Training Program (2014000020124G029), the Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (BUAA-VR-14KF-04).

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Authors and Affiliations

  1. Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, People’s Republic of China

    Hongqian Chen, Yi Fang & Yi Chen

  2. College of Management, Beijing Union University, Beijing, People’s Republic of China

    Hui Li

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  1. Hongqian Chen
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  2. Hui Li
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  3. Yi Fang
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  4. Yi Chen
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Correspondence to Hui Li.

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Chen, H., Li, H., Fang, Y. et al. Anisotropic parallel coordinates with adjustment based on distribution features. J Vis 19, 327–335 (2016). https://doi.org/10.1007/s12650-015-0320-z

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  • DOI: https://doi.org/10.1007/s12650-015-0320-z

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