Overview
- 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
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 144)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (13 chapters)
-
Front Matter
Keywords
About this book
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.
Reviews
“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)
Authors and Affiliations
Bibliographic Information
Book Title: Visual Knowledge Discovery and Machine Learning
Authors: Boris Kovalerchuk
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-319-73040-0
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2018
Hardcover ISBN: 978-3-319-73039-4Published: 26 January 2018
Softcover ISBN: 978-3-319-89230-6Published: 04 June 2019
eBook ISBN: 978-3-319-73040-0Published: 17 January 2018
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XXI, 317
Number of Illustrations: 11 b/w illustrations, 263 illustrations in colour