Details
Visual Knowledge Discovery and Machine Learning
Intelligent Systems Reference Library, Band 144
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171,19 € |
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| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 17.01.2018 |
| ISBN/EAN: | 9783319730400 |
| Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
<p>This book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, which preserve the n-D information. This class of visual representations, called the General Lines Coordinates (GLCs), is accompanied by a set of algorithms for n-D data classification, clustering, dimension reduction, and Pareto optimization. The mathematical and theoretical analyses and methodology of GLC are included, and the usefulness of this new approach is demonstrated in multiple case studies. These include the Challenger disaster, world hunger data, health monitoring, image processing, text classification, market forecasts for a currency exchange rate, computer-aided medical diagnostics, and others. As such, the book offers a unique resource for students, researchers, and practitioners in the emerging field of Data Science.</p>
Motivation, Problems and Approach.- General Line Coordinates (GLC).- Theoretical and Mathematical Basis of GLC.- Adjustable GLCs for decreasing occlusion and pattern simplification.- GLC Case Studies.- Discovering visual features and shape perception capabilities in GLC.- Interactive Visual Classification, Clustering and Dimension Reduction with GLC-L.- Knowledge Discovery and Machine Learning for Investment Strategy with CPC.<p><b></b></p><p><b></b></p><p><b></b></p><p><b></b></p>
<p>This book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossless 2-D and 3-D visualization methods, which preserve the n-D information. This class of visual representations, called the General Lines Coordinates (GLCs), is accompanied by a set of algorithms for n-D data classification, clustering, dimension reduction, and Pareto optimization. The mathematical and theoretical analyses and methodology of GLC are included, and the usefulness of this new approach is demonstrated in multiple case studies. These include the Challenger disaster, world hunger data, health monitoring, image processing, text classification, market forecasts for a currency exchange rate, computer-aided medical diagnostics, and others. As such, the book offers a unique resource for students, researchers, and practitioners in the emerging field of Data Science.</p>
Expands methods of knowledge discovery based on visual means Generates new lossless visual representations of n-D data in 2-D that fully preserve n-D data with a focus on machine learning/data mining goals, in contrast to a generic visualization without a clearly specified goal Effectively uses human shape perception capabilities in mapping n-D data points into 2-D graphs Identifies n-D data structures such as hyper-tubes, hyperplanes, hyper-spheres, etc. using lossless visual data representations
<div>Expands methods of knowledge discovery based on visual means </div><div><br></div><div>Generates new lossless visual representations of n-D data in 2-D that fully preserves n-D data with focus on Machine Learning/ Data Mining goals, in contrast with a generic visualization without a clearly specified goal</div><div><br></div><div>Provides clear interpretation of features of visual representations in terms of n-D data properties</div><div><br></div><div>Effectively usrees human vision capabilities of shape perception in mapping n-D data points into 2-D graphs</div><div><br></div><div>Recognizes n-D data structures such as hyper–tubes, hyper-planes, hyper-spheres, etc. using lossless visual data representations</div><div><br></div>
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