Application of Andrew’s Plots to Visualization of Multidimensional Data

APA 6th edition
In-text: (Grinshpun, 2016)
Your Bibliography: Grinshpun, V. (2016). Application of Andrew’s Plots to Visualization of Multidimensional Data . International Journal of Environmental and Science Education, 11(17), 10539-10551.

Harvard
In-text: (Grinshpun, 2016)
Your Bibliography: Grinshpun, V. (2016). Application of Andrew’s Plots to Visualization of Multidimensional Data . International Journal of Environmental and Science Education, 11(17), pp. 10539-10551.

Chicago 16th edition
In-text: (Grinshpun 2016)
Your Bibliography: Grinshpun, Vadim (2016). "Application of Andrew’s Plots to Visualization of Multidimensional Data ". International Journal of Environmental and Science Education 11 (17):10539-10551.

Abstract

Importance: The article raises a point of visual representation of big data, recently considered to be demanded for many scientific and real-life applications, and analyzes particulars for visualization of multi-dimensional data, giving examples of the visual analytics-related problems. Objectives: The purpose of this paper is to study application of Andrews plots to visualization of multidimensional data. Methods: Application of Andrews plots to multidimensional data visualization is investigated herein by means of analysis, logical generalization, scientific abstraction. Results: The direct interaction between the analyst and the visualization system projecting the multi-dimensional data into spaces with fewer dimensions, supporting formulation and testing of the hypotheses regarding the nature and the data structure have been researched. The article seems to be useful for working with multidimensional dataset to optimize the process.

Keywords: Multi-criteria optimization, big data, multi-dimensional space, visual analytics

References

Andrews, D.F. (1972). Plots of high-dimensional data. Biometrics, 28, 69-97.

Assuncao, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A.S., Buyya, R. (2014) Big Data Computing and Clouds: Challenges, Solutions, and Future Directions. Journal of Parallel and DistributedComputing, 79-80, 3-15.

Baker, M.P. & Wickens, C.D. (1995) Human Factors in Virtual Environments for the Visual Analysis of Scientific Data. Technical Report. – NCSA. draft. http://monet.ncsa.uiuc.edu/~baker/PNL/paper.html

Belous, V.V., Groshev, S.V., Karpenko, A.P. & Ostroushko, V.A. (2015). Imaging methods for Pareto front in the problem of multi-criteria optimization. Overview. 20 Baikal'skaya Vserossiiskaya konferentsiya “Informatsionnye i matematicheskie tekhnologii v nauke i upravlenii”. Proc. of the 20th Baikal Conference on Information and Mathematical Technologies in Science and Management. Irkutsk, 22-29.

Big Data Visualization. (2013). Turning Big Data into Big Insights. The Rise of Visualization-based Data Discovery Tools. http://www.intel.ru/content/dam/www/public/us/en/documents/white-papers/big-data-visualization-turning-big-data-into-big-insights.pdf

Dasgupta, A., Chen, M., Kosara, R. (2012). Conceptualizing Visual Uncertainty in Parallel Coordinates. Comput. Graph. Forum, 31(3), 1015-1024.

Dillon W.R., Goldstein M.(1984). Multivariate Analysis: Methods and Applications. New York: Wiley.

Embrechts, P. & Herzberg, A.M. (1991). Variations of Andrews' Plots. International Statistical Review, 59(2), 175-194.

Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7, 179-188.

Fout, N. & Ma, K.L. (2012) Reliable Visualization: Verification of Visualization based on Uncertainty Analysis. Los Angeles : Tech. rep., University of California. – Davis.

Keim, D.A., Mansmann, F., Schneidewind, J., Thomas, J. & Ziegler, H. (2008a) Visual analytics: Scope and challenges. San Francisco : Visual Data Mining.

Keim, D. Qu, H. & Ma, K.L. (2013) Big-Data Visualization. IEEE Computer Graphics and Applications, July/August.

Keim, D., Andrienko, G., Fekete, J.-D., Gorg, C, Kohlhammer, J., and Melancon, G. (2008b) Visual Analytics: Definition, Process, and Challenges. Information Visualisation, Springer-Verlag, Berlin Heidelberg, 4950, 154-175.

Keim, D., Kohlhammer, J., Ellis, G. & Mansmann, F. (2010). Mastering the Information Age – Solving Problems with Visual Analytics. http://www.vismaster.eu/wp-content/uploads/2010/11/VisMaster-book-lowres.pdf

Kielman, J. & Thomas, J. (2009). Special Issue: Foundations and Frontiers of Visual Analytics, Information Visualization, 8(4), 239-314.

Li-Xin, & Wang. (2003). The WM method completed: a flexible fuzzy system approach to data mining. IEEE Trans. Fuzzy Systems, 11(6), 768-782.

Maletic, J.I., Marcus, A. & Collard, M.L. (2002). A task oriented view of software visualization. International Workshop on Visualizing Software for Understanding and Analysis, 32-40.

Mamdani, E.H. (2008). Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis. IEEE Trans. Computers, 12, 1182-1191.

Manakov, D., Mukhachev, A. & Shinkevich, A. (2003). Visualization of the Distributed Data of Huge Volume. Assembly, Filtration, Sorting. Proceedings of the 13-th International Conference on Computer Graphics and Vision Graphicon-2003, 198-201.

North, Ch. (2006). Toward Measuring Visualization Insight. IEEE Computer Graphics and Applications, 26(3), 20-23.

Shneiderman, B. (1996). The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. Proceedings of the IEEE Conference on Visual Languages, 336-343.

Shneiderman, B. (2014). The big picture for big data. San Francisco : Science.

Thomas, J. & Cook, К. (2005) Illuminating the Path: Research and Development Agenda for Visual Analytics San Francisco : IEEE-Press.

Wang, L.X. & Mendel, J.M. (1992). Generating fuzzy rules by learning from examples. IEEE Trans. Syst., Man, Cybern, 22(6), 1414-1427.

Zadeh, L.A. (1994). Fuzzy logic, neural networks, and soft computing. Communications of the ACM, 3, 77-84.