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Daniel A. Keim, "Designing PixelOriented Visualization Techniques: Theory and Applications," IEEE Transactions on Visualization and Computer Graphics, vol. 6, no. 1, pp. 5978, JanuaryMarch, 2000.  
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@article{ 10.1109/2945.841121, author = {Daniel A. Keim}, title = {Designing PixelOriented Visualization Techniques: Theory and Applications}, journal ={IEEE Transactions on Visualization and Computer Graphics}, volume = {6}, number = {1}, issn = {10772626}, year = {2000}, pages = {5978}, doi = {http://doi.ieeecomputersociety.org/10.1109/2945.841121}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Visualization and Computer Graphics TI  Designing PixelOriented Visualization Techniques: Theory and Applications IS  1 SN  10772626 SP59 EP78 EPD  5978 A1  Daniel A. Keim, PY  2000 KW  Information visualization KW  visualizing large data sets KW  visualizing multidimensional and multivariate data KW  visual data exploration KW  visual data mining. VL  6 JA  IEEE Transactions on Visualization and Computer Graphics ER   
Abstract—Visualization techniques are of increasing importance in exploring and analyzing large amounts of multidimensional information. One important class of visualization techniques which is particularly interesting for visualizing very large multidimensional data sets is the class of pixeloriented techniques. The basic idea of pixeloriented visualization techniques is to represent as many data objects as possible on the screen at the same time by mapping each data value to a pixel of the screen and arranging the pixels adequately. A number of different pixeloriented visualization techniques have been proposed in recent years and it has been shown that the techniques are useful for visual data exploration in a number of different application contexts. In this paper, we discuss a number of issues which are of high importance in developing pixeloriented visualization techniques. The major goal of this article is to provide a formal basis of pixeloriented visualization techniques and show that the design decisions in developing them can be seen as solutions of welldefined optimization problems. This is true for the mapping of the data values to colors, the arrangement of pixels inside the subwindows, the shape of the subwindows, and the ordering of the dimension subwindows. The paper also discusses the design issues of special variants of pixeloriented techniques for visualizing large spatial data sets. The optimization functions for the mentioned design decisions are important for the effectiveness of the resulting visualizations. We show this by evaluating the optimization functions and comparing the results to the visualizations obtained in a number of different application.
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