Journal of Visualization

, Volume 20, Issue 1, pp 163–179 | Cite as

An improved diversity visualization system for multivariate data

  • Mee Chin WeeEmail author
Regular Paper


Exploring and analyzing data is becoming increasingly difficult due to the growth of data. Visual analytics tools can be an attractive solution to support the process to derive insights from data. Currently, there are many visual representation methods to visualize the diversity in multivariate data sets. However, most of these applications focus on visual representation problems, and these solutions support limited interactive components for users to effectively explore and analyze data on screen. In this paper, the adaptive diversity table (ADT) is proposed to solve the visual representation problems (occlusion and technique interference). Furthermore, it integrates the mantra techniques to support users to accomplish seven important tasks (i.e. overview, zoom, filter, details-on-demand, relate, history, and extract) that are useful for high dimensional data exploration and data analysis. Experimental results show that the proposed ADT is a better visual representation as compared to other prior techniques. Majority of the respondents prefers to use the proposed ADT over the other visual representation methods. User studies also show that the proposed ADT is more useful as it enables the respondents to be more efficient in analyzing the data sets provided.

Graphical Abstract


Data visualization Visual analytics Big data Visual information seeking mantra 



This research is supported by the Research Grants (project number RG053-11ICT, RG102-12ICT) from the University of Malaya.

Supplementary material

Supplementary material 1 (MP4 13987 kb)


  1. Alan JK (1975) Pielou, E. C. 1975. Ecological diversity. John Wiley & Sons, New York, viii + 165 p. $14.95. Limnol Oceanogr 22(1):174. doi: 10.4319/lo.1977.22.1.0174b
  2. Alexander Dr Ulrike, Spree F, Maria Brunetti J, Garcia R (2014) User-centered design and evaluation of overview components for semantic data exploration. Aslib J Inf Manag 66(5):519–536CrossRefGoogle Scholar
  3. Baudisch P, Good N, Stewart P (2001) Focus plus context screens: combining display technology with visualization techniques. In: Proceedings of the 14th annual ACM symposium on User interface software and technology, pp 31–40Google Scholar
  4. Bendi F, Kosara R, Hauser H (2005) Parallel sets: visual analysis of categorical data. In: IEEE symposium on information visualization (INFOVIS), pp 133–140Google Scholar
  5. Beyer J, Hadwiger M, Al-Awami A, Jeong WK, Kasthuri N, Lichtman JW, Pfister H (2013) Exploring the connectome: petascale volume visualization of microscopy data streams. IEEE Comput Graph Appl 33(4):50–61CrossRefGoogle Scholar
  6. Birinci M, Kiranyaz S (2014) A perceptual scheme for fully automatic video shot boundary detection. Signal Process Image Commun 29(3):410–423CrossRefGoogle Scholar
  7. Brito PQ, Soares C, Almeida S, Monte A, Byvoet M (2015) Customer segmentation in a large database of an online customized fashion business. J Robot Comput Integr Manuf 36(C):93–100Google Scholar
  8. Castellanos-Garzn JA, Garca CA, Novais P, Daz F (2013) A visual analytics framework for cluster analysis of dna microarray data. Expert Syst Appl 40(2):758–774CrossRefGoogle Scholar
  9. Chen W, Guo F, Wang FY (2015) A survey of traffic data visualization. Intell Transp Syst IEEE Trans 16(6):2970–2984CrossRefGoogle Scholar
  10. Choo J, Park H (2013) Customizing computational methods for visual analytics with big data. IEEE Comput Graph Appl 33(4):22–28CrossRefGoogle Scholar
  11. Congress U (1987) Technologies to maintain biological diversity. Office of Technology Assessment, Washington, DCGoogle Scholar
  12. Conover WJ, Iman RL (1981) Rank transformations as a bridge between parametric and nonparametric statistics. Am Stat 35(3):124–129zbMATHGoogle Scholar
  13. Costabile MF, Semeraro G (1998) Information visualization in the interaction with idl. In: ERCIM, pp 73Google Scholar
  14. Garca-Borroto M, Martnez-Trinidad JF, Carrasco-Ochoa JA (2015) Finding the best diversity generation procedures for mining contrast patterns. Expert Syst Appl 42(11):4859–4866CrossRefGoogle Scholar
  15. Harrower M, Brewer CA (2003) Colorbrewer. org: an online tool for selecting colour schemes for maps. Cartogr J 40(1):27–37CrossRefGoogle Scholar
  16. Heip C (1974) A new index measuring evenness. J Mar Biol Assoc UK 54(03):555–557CrossRefGoogle Scholar
  17. Janicki J, Guo C, Conway M, Donohue R, Roth RE (2014) Weevil viewer: an interactive mapping application for geographic and phenological exploration of wisconsin’s primitive weevils. J Maps 10(2):289–296CrossRefGoogle Scholar
  18. Keim D (2002) Information visualization and visual data mining. IEEE Trans Vis Comput Graph 8(1):1–8MathSciNetCrossRefGoogle Scholar
  19. Keim D, Andrienko G, daniel Fekete J, Kohlhammer J, Cedex FO (2008) Visual analytics: definition, process, and challenges. In: Information visualization: human-centered issues and perspectives, pp 154–175Google Scholar
  20. Kidwell P, Lebanon G, Cleveland WS (2008) Visualizing incomplete and partially ranked data. IEEE Trans Vis Comput Graph 14(6):1356–1363CrossRefGoogle Scholar
  21. Ko S, Maciejewski R, Jang Y, Ebert DS (2012) Marketanalyzer: an interactive visual analytics system for analyzing competitive advantage using point of sale data. Comput Graph Forum 31(3pt3):1245–1254Google Scholar
  22. Kwon O, Sim JM (2013) Effects of data set features on the performances of classification algorithms. Expert Syst Appl 40(5):1847–1857CrossRefGoogle Scholar
  23. Lee TY, Tong X, Shen HW, Wong PC, Hagos S, Leung LR (2013) Feature tracking and visualization of the madden-julian oscillation in climate simulation. IEEE Comput Graph Appl 33(4):29–37CrossRefGoogle Scholar
  24. Liao Sh, Yj Chen, Yt Lin (2011) Mining customer knowledge to implement online shopping and home delivery for hypermarkets. Expert Syst Appl 38(4):3982–3991CrossRefGoogle Scholar
  25. Liao Sh, Chu Ph, Yj Chen, Chang CC (2012) Mining customer knowledge for exploring online group buying behavior. Expert Syst Appl 39(3):3708–3716CrossRefGoogle Scholar
  26. Lichman M (2013) UCI machine learning repository.
  27. Liu S, Cui W, Wu Y, Liu M (2014) A survey on information visualization: recent advances and challenges. Vis Comput 30(12):1373–1393CrossRefGoogle Scholar
  28. Maletic J, Leigh J, Marcus A, Dunlap G, et al (2001) Visualizing object-oriented software in virtual reality. In: Proceedings of the 9th international workshop on program comprehension (IWPC), pp 26–35Google Scholar
  29. Newman DJ, Hettich S, Blake CL, Merz CJ (1998) Uci repository of machine learning databases. Department of Information and Computer Sciences, University of California, Irvine.
  30. Pearlman J, Rheingans P, Des Jardins M (2007) Visualizing diversity and depth over a set of objects. IEEE Comput Graph Appl 27(5):35–45CrossRefGoogle Scholar
  31. Pham T, Hess R, Ju C, Zhang E, Metoyer R (2010) Visualization of diversity in large multivariate data sets. IEEE Trans Vis Comput Graph 16(6):1053–1062CrossRefGoogle Scholar
  32. Pham T, Jones J, Metoyer R, Swanson F, Pabst R (2013) Interactive visual analysis promotes exploration of long-term ecological data. Ecosphere 4(9):112CrossRefGoogle Scholar
  33. Podowski RM, Miller B, Wasserman WW (2006) Visualization of complementary systems biology data with parallel heatmaps. IBM J Res Dev 50(6):575–581CrossRefGoogle Scholar
  34. Reda K, Febretti A, Knoll A, Aurisano J, Leigh J, Johnson A, Papka ME, Hereld M (2013) Visualizing large, heterogeneous data in hybrid-reality environments. IEEE Comput Graph Appl 4:38–48CrossRefGoogle Scholar
  35. Rhyne T, Chen M (2013) Cutting-edge research in visualization. Computer 46(5):22–24CrossRefGoogle Scholar
  36. Roth RE, MacEachren AM (2016) Geovisual analytics and the science of interaction: an empirical interaction study. Cartogr Geogr Inf Sci 43(1):30–54. doi: 10.1080/15230406.2015.1021714 CrossRefGoogle Scholar
  37. Seo J, Shneiderman B (2004) A rank-by-feature framework for unsupervised multidimensional data exploration using low dimensional projections. In: IEEE symposium on information visualization (INFOVIS), pp 65–72Google Scholar
  38. Shneiderman B (1996) The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of the IEEE symposium on visual languages, pp 336–343Google Scholar
  39. Tory M, Potts S, Möller T (2005) A parallel coordinates style interface for exploratory volume visualization. IEEE Trans Vis Comput Graph 11(1):71–80CrossRefGoogle Scholar
  40. Whittaker RH (1965) Dominance and diversity in land plant communities numerical relations of species express the importance of competition in community function and evolution. Science 147(3655):250–260CrossRefGoogle Scholar
  41. Woniak M, Graa M, Corchado E (2014) A survey of multiple classifier systems as hybrid systems. Inf Fusion 16:3–17CrossRefGoogle Scholar
  42. Yang F, Li X, Li Q, Li T (2014) Exploring the diversity in cluster ensemble generation: random sampling and random projection. Expert Syst Appl 41(10):4844–4866CrossRefGoogle Scholar
  43. Zeileis A, Hornik K, Murrell P (2009) Escaping rgbland: selecting colors for statistical graphics. Comput Stat Data Anal 53(9):3259–3270MathSciNetCrossRefzbMATHGoogle Scholar
  44. Zimmerman DW, Zumbo BD (1993) Rank transformations and the power of the student t test and welch t’test for non-normal populations with unequal variances. Can J Exp Psychol/Revue canadienne de psychologie expérimentale 47(3):523CrossRefGoogle Scholar

Copyright information

© The Visualization Society of Japan 2016

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia

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