1st ed. 2018, XXI, 317 p. 274 illus.,

263 illus. in color.

Springer Flier



Boris Kovalerchuk

Visual Knowledge Discovery

and Machine Learning

Series: Intelligent Systems Reference Library


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, hyperspheres, etc. using lossless visual data representations


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.