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Visual Knowledge Discovery and Machine Learning

  • Boris┬áKovalerchuk

Part of the Intelligent Systems Reference Library book series (ISRL, volume 144)

Table of contents

About this book

Introduction

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.

Keywords

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

Authors and affiliations

  • Boris┬áKovalerchuk
    • 1
  1. 1.Central Washington UniversityEllensburgUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-73040-0
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-73039-4
  • Online ISBN 978-3-319-73040-0
  • Series Print ISSN 1868-4394
  • Series Online ISSN 1868-4408
  • Buy this book on publisher's site