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Axes Re-Ordering in Parallel Coordinate for Pattern Optimization

by Hemant Makwana, Sanjay Tanwani, Suresh Jain
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 40 - Number 13
Year of Publication: 2012
Authors: Hemant Makwana, Sanjay Tanwani, Suresh Jain
10.5120/5044-7370

Hemant Makwana, Sanjay Tanwani, Suresh Jain . Axes Re-Ordering in Parallel Coordinate for Pattern Optimization. International Journal of Computer Applications. 40, 13 ( February 2012), 43-48. DOI=10.5120/5044-7370

@article{ 10.5120/5044-7370,
author = { Hemant Makwana, Sanjay Tanwani, Suresh Jain },
title = { Axes Re-Ordering in Parallel Coordinate for Pattern Optimization },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 40 },
number = { 13 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 43-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume40/number13/5044-7370/ },
doi = { 10.5120/5044-7370 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:28:01.601246+05:30
%A Hemant Makwana
%A Sanjay Tanwani
%A Suresh Jain
%T Axes Re-Ordering in Parallel Coordinate for Pattern Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 40
%N 13
%P 43-48
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Visualization of multidimensional dataset is a challenging task due to non-uniformity of the data. It requires new ways to display data for better analysis and interpretation. Parallel coordinate is one of the popular techniques for visualization of multi dimensional dataset. Parallel coordinate technique emphasis various types of patterns present in the dataset. Here, pattern is shown by a poly-line. Slope of poly-line indicates the difference between data values. Variation in slope creates the different types of pattern. Based on slope, pattern can be classified and this kind of classification helps to explore distinct pattern available in dataset. Ordering of the axis affects pattern available in any dataset. Specific arrangement of axis may provide maximum patterns and another arrangement may provide minimum patterns. Ordering of axis in different order to find maximum or minimum pattern requires exponential time. Here, we propose a novel clustering technique using heuristic based branch & bound based axis reordering mechanism to solve this problem in polynomial time.

References
  1. Ellis G., Dix A., “A taxonomy of clutter reduction for information visualization.” IEEE Trans. on Vis. And Comp. Graph. 13, 6, 1216–1223, (2007).
  2. H. Zhou, X. Yuan, H. Qu, W. Cui, and B. Chen. “Visual clustering in parallel coordinates”, Computer Graphics Forum, 27(3): 1047-1054, 2008.
  3. D. Hand, H. Mannila, and P. Smyth. “Principles of Data Mining.” MIT Press, 2001.
  4. G. Ellis and A. Dix. “Enabling automatic clutter reduction in parallel coordinate plots.” IEEE Transactions on Visualization and Computer Graphics, 12(5), 717–724, 2006.
  5. T. Hastie, R. Tibshirani, and J. Friedman. “The Elements of Statistical Learning.” Springer-Verlag, 2001.
  6. G. Andrienko and N. Andrienko. “Parallel coordinates for exploring properties of subsets.” In 2nd IEEE International Conference on Coordinated and Multiple Views in Exploratory Visualization, pages 93–104, 2004.
  7. M. Novotny. “Visually effective information visualization of large data.” In 8th Central European Seminar on Computer Graphics (CESCG 2004), pages 41–48, 2004.
  8. J. J. Miller and E. J. Wegman. “Construction of line densities for parallel coordinate plots.” Computing and Graphics in Statistics, pages 107–123, 1992.
  9. E. J. Wegman and Q. Luo. “High dimensional clustering using Parallel coordinates and the grand tour.” Technical Report 124, Fairfax, Virginia 22030, U.S.A., 1996.
  10. J. F. Rodriques Jr., A. J. Traina, and C. Traina Jr. “Frequency plot and relevance plot to enhance visual data exploration.” In XVI Brazilian Symposium on Computer Graphics and Image Processing, pages 117– 124, 2003.
  11. A.K. Jain, M.N. Murty and P.J. Flynn, “Data Clustering: A Review.” ACM Computing Surveys, 31(3), pages 264-323, Sept 1999.
  12. F. Murtagh, "Clustering in Massive Data Sets", Chemical Data Analysis in the Large, Proc. Beilstein-Institut Workshop, May, 2000,
  13. ZHOU H., YUAN X., QU H., CUI W., and CHEN B. “Visual clustering in parallel coordinates.” Computer Graphics Forum 27, 3, 1047–1054, 2008.
  14. Wei Peng, Matthew O. Ward and Elke A. Rundensteiner, "Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering." IEEE Symposium on Information Visualization 2004 (InfoVis 2004), pp 89 - 96, October 2004.
  15. Y. H. Fua, M. O.Ward, and E. A. Rundensteiner. “Hierarchical parallel coordinates for exploration of large datasets.” In Proc. Visualization’99, pages 43–50, 1999.
  16. J. Yang, M.O. Ward, E.A. Rundensteiner and S. Huang. “Interactive hierarchical displays: a general framework for visualization and exploration of large multivariate data sets.” Computers and Graphics, 27(2), pp. 265-283, Apr 2003.
  17. A.O. Artero, M.C. Ferreira de Oliveira and H. Levkowitz.Uncovering, “Clusters in Crowded Parallel Coordinates Visualizations.” Infovis, pp. 131-136, 2004
  18. L. Zhang, C. Tang, Y. Shi, Y. Song, A. Zhang and M.Ramanathan, “VizCluster and Its Application on Clustering Gene Expression Data.” Distributed and Parallel Databases, 13(1) , pp. 73-97, 2003,
  19. Ruth Rosenholtz, Yuanzhen Li, Jonathan Mansfield, and Zhenlan Jin, “Feature congestion: a measure of display clutter.” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, page761, 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Visualization Parallel Coordinates Cluttering Clustering Outlier pattren

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