Read e-book online Pixelization Paradigm: First Visual Information Expert PDF

By Pierre P. Lévy (auth.), Pierre P Lévy, Bénédicte Le Grand, François Poulet, Michel Soto, Laszlo Darago, Laurent Toubiana, Jean-François Vibert (eds.)

ISBN-10: 3540710264

ISBN-13: 9783540710264

ISBN-10: 3540710272

ISBN-13: 9783540710271

The pixelization paradigm states as a postulate that pixelization tools are wealthy and are worthy exploring so far as attainable. in reality, we expect that the power of those equipment lies of their simplicity, of their high-density approach of data illustration estate and of their compatibility with neurocognitive approaches. • Simplicity, simply because pixelization belongs to two-dimensional details visualization tools and its major suggestion is deciding upon a “pixel” with an informational entity on the way to translate a collection of informational entities into a picture. • High-density manner of knowledge illustration estate, first of all simply because pixelization illustration features a 3rd dimension—each pixel’s color—and secondly simply because pixelization is a “compact” (two-dimensional) manner of representing info in comparison with linear one-dimensional representations (Ganascia, p.255) . • Compatibility with neurocognitive strategies, to start with simply because we're thr- dimensional beings and therefore we're intrinsically larger at greedy one- or two-dimensional facts, and secondly as the cerebral cortex is usually a bi-dimensional constitution the place metaphorically the neurons may be assimilated to “pixels,” whose task performs the position of colour (Lévy, p.3). The pixelization paradigm could be studied alongside similar instructions: pixelization and its implementation and pixelization and cognition. the 1st direction—pixelization and its implementation—may be divided into elements: pixelization idea and pixelization application.

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Extra resources for Pixelization Paradigm: First Visual Information Expert Workshop, VIEW 2006, Paris, France, April 24-25, 2006, Revised Selected Papers

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A) (b) Fig. 3. (a) Glass data set-all attributes used. (b) Glass data set-attributes 3, 4, 8, 9. Using Biclustering for Automatic Attribute Selection to Enhance Global Visualization 43 For selecting the attributes so as to enhance visualization, biclustering was performed on the glass data set. e. Na, Mg, Ca and Ba. Using these attributes the similarity matrix was again generated, and the color coded matrix is shown in Figu-3(b). Observe the significantly enhanced visualization with lots of interesting relationships and clusters distinctly visible.

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Pixelization Paradigm: First Visual Information Expert Workshop, VIEW 2006, Paris, France, April 24-25, 2006, Revised Selected Papers by Pierre P. Lévy (auth.), Pierre P Lévy, Bénédicte Le Grand, François Poulet, Michel Soto, Laszlo Darago, Laurent Toubiana, Jean-François Vibert (eds.)


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