Visual Knowledge Discovery and Machine Learning
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Beschreibung
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.
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 n-D data structures such as hyper–tubes, hyper-planes, hyper-spheres, etc. using lossless visual data representations
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
Autor*in
Boris Kovalerchuk
Themen in »Visual Knowledge Discovery and Machine Learning«
Intelligent Systems Data Science Knowledge Discovery Visual Data Mining Machine Learning Multidimensional Data Visualization Lossless Visual Representation General Line Coordinates Collocated Coordinates Paired Coordinates Shifted Coordinates Parallel Coordinates Collaborative Visualization
Stimmen zu »Visual Knowledge Discovery and Machine Learning«
“The book is a good suggestion for a data scientist or someone who would like to specialise on GLCs … it provides a helpful introduction along with a wide variety of case studies that help any scientist to familiarise with this method.” (Angeliki Katsenou, Perception, Vol. 47 (12), December, 2018)
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