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Visualization and Data Mining for High Dimensional Data

–With Connections to Information Retrieval

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Information Retrieval (RuSSIR 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 505))

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Abstract

The first, and still more popular application, of parallel coordinates is in exploratory data analysis (EDA); discovering data subsets (relations) satisfying given objectives.

A large collection of methodologies tracing the development of the field can be found in [6].

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Notes

  1. 1.

    The venerable name “Exploratory Data Analysis” EDA is used interchangeably with the currently more fashionable “Visual Data Mining”.

  2. 2.

    MDG’s Ltd proprietary software–All Rights Reserved, is used by permission.

  3. 3.

    I am grateful to Prof. R. Coiffman and his group at the CS & Math. Depts at Yale University for giving me this dataset.

References

  1. Bassett, E.W.: IBM’s IBM fix. Ind. Comput. 14(41), 23–25 (1995)

    Google Scholar 

  2. Becker, S., Hinton, G.: A self-organizing neural network that discovers surfaces in random-dot stereograms. Nature (Lond.) 355, 161–163 (1992)

    Article  Google Scholar 

  3. Bollobas, B.: Graph Theory. Springer, New York (1979)

    Book  MATH  Google Scholar 

  4. Eickemeyer, J.: Visualizing p-flats in N-space using parallel coordinates. Ph.D. thesis, Dept. Comp. Sc., UCLA (1992)

    Google Scholar 

  5. Fayad, G., Piatesky-Shapiro, U.M., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, Cambridge Mass. (1996)

    Google Scholar 

  6. Friendly, M., et al.: Milestones in Thematic Cartography (2005). www.math.yorku.ca/scs/SCS/Gallery/milestones/

  7. Fyfe, C., Lai, P.L.: ICA using kernel canonical correlation analysis. In: ICA 2000 (2000)

    Google Scholar 

  8. Gennings, C., Dawson, K.S., Carter, W.H., Myers, R.H.: Interpreting plots of a multidimensional dose-response surface in parallel coordinates. Biometrics 46, 719–35 (1990)

    Article  Google Scholar 

  9. Harary, F.: Graph Theory. Addison-Wesley, Reading (1969)

    Book  MATH  Google Scholar 

  10. Hurley, C.B., Oldford, R.W.: Pairwise display of high-dimensional information via eulerian tours and hamiltonian decompositions. J. Comput. Graph. Stat. 19(4), 861–886 (2010)

    Article  Google Scholar 

  11. Inselberg, A.: Visual data mining with parallel coordinates. Comput. Stat. 13(1), 47–64 (1998)

    MATH  Google Scholar 

  12. Inselberg, A.: Parallel Coordinates: VISUAL Multidimensional Geometry and its Applications. Springer, New York (2009)

    Book  MATH  Google Scholar 

  13. Inselberg, A., Avidan, T.: The automated multidimensional detective. In: Proceedings of IEEE Information Visualization 1999, pp. 112–119. IEEE Comp. Soc., Los Alamitos (1999)

    Google Scholar 

  14. Inselberg, A., Lai, P.L.: Visualizing families of close planes, 66. In: Proceedings of the 5th Asian Conference on Statistics, Hong Kong (2005)

    Google Scholar 

  15. Lai, P.L., Leen, G., Fyfe, C.: The sphere-concatenate method for gaussian process canonical correlation analysis. In: Oja, E., Kollias, S.D., Stafylopatis, A., Duch, W. (eds.) ICANN 2006. LNCS, vol. 4132, pp. 302–310. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  16. Lucas, D.E.: Recréations Mathematiques, vol. II. Gauthier Villars, Paris (1892)

    MATH  Google Scholar 

  17. Mardia, K.V., Kent, J.T., Bibby, J.M.: Multivariate Analysis. Academic Press, London (1979)

    MATH  Google Scholar 

  18. Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  19. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufman, San Francisco (1993)

    Google Scholar 

  20. Stone, J.: Learning perpetually salient visual parameters using spationtemporal smoothness constraints. Neural Comput. 8(7), 1463–1492 (1996)

    Article  Google Scholar 

  21. Tufte, E.R.: The Visual Display of Quantitative Information. Graphic Press, Connecticut (1983)

    Google Scholar 

  22. Tufte, E.R.: Envisioning Information. Graphic Press, Connecticut (1990)

    Google Scholar 

  23. Tufte, E.R.: Visual Explanation. Graphic Press, Connecticut (1996)

    Google Scholar 

  24. Ying, W., Fyfe, C., Lai, P.L.: Two forms of immediate reward reinforcement learning for exploratory data analysis. Neural Netw. 21(6), 847–855 (2008)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel

    Alfred Inselberg

  2. Department of Electronic Engineering, Southern Taiwan University of Science and Technology, Tainan, Taiwan

    Pei Ling Lai

Authors
  1. Alfred Inselberg
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  2. Pei Ling Lai
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Corresponding author

Correspondence to Alfred Inselberg .

Editor information

Editors and Affiliations

  1. Ural Federal University, Yekaterinburg, Russia

    Pavel Braslavski

  2. National Research University Higher School of Economics, Nizhniy Novgorod, Russia

    Nikolay Karpov

  3. Intelligent Systems Laboratory, University of Amsterdam, Amsterdam, The Netherlands

    Marcel Worring

  4. Barcelona Media Research Foundation, Barcelona, Spain

    Yana Volkovich

  5. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

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Inselberg, A., Lai, P.L. (2015). Visualization and Data Mining for High Dimensional Data. In: Braslavski, P., Karpov, N., Worring, M., Volkovich, Y., Ignatov, D.I. (eds) Information Retrieval. RuSSIR 2014. Communications in Computer and Information Science, vol 505. Springer, Cham. https://doi.org/10.1007/978-3-319-25485-2_4

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  • DOI: https://doi.org/10.1007/978-3-319-25485-2_4

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  • Online ISBN: 978-3-319-25485-2

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