Visual Knowledge Discovery and Machine Learning
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181,89 €
Intelligent Systems Reference Library 144
| ISBN: |
3319730398 |
| ISBN 13: |
9783319730394 |
| Autor: |
Kovalerchuk, Boris |
| Verlag: |
Springer Verlag GmbH |
| Umfang: |
xxi, 317 S., 11 s/w Illustr., 263 farbige Illustr., 317 p. 274 illus., 263 illus. in color. |
| Erscheinungsdatum: |
26.01.2018 |
| Auflage: |
1/2018 |
| Produktform: |
Gebunden/Hardback |
| Einband: |
Gebunden |
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.
Beschreibung
Expands methods of knowledge discovery based on visual means 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 Provides clear interpretation of features of visual representations in terms of n-D data properties Effectively usrees human vision capabilities of shape perception in mapping n-D data points into 2-D graphs Recognizes nD data structures such as hypertubes, hyperplanes, hyperspheres, etc. using lossless visual data representations
Herstellerkennzeichnung:
Springer Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE
E-Mail: juergen.hartmann@springer.com
