Multidimensional Data Visualization

Methods and Applications

  • Gintautas Dzemyda
  • Olga Kurasova
  • Julius Žilinskas

Part of the Springer Optimization and Its Applications book series (SOIA, volume 75)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Gintautas Dzemyda, Olga Kurasova, Julius Žilinskas
    Pages 1-4
  3. Gintautas Dzemyda, Olga Kurasova, Julius Žilinskas
    Pages 5-40
  4. Gintautas Dzemyda, Olga Kurasova, Julius Žilinskas
    Pages 41-112
  5. Gintautas Dzemyda, Olga Kurasova, Julius Žilinskas
    Pages 113-177
  6. Gintautas Dzemyda, Olga Kurasova, Julius Žilinskas
    Pages 179-226
  7. Back Matter
    Pages 227-250

About this book

Introduction

The goal of this book is to present a variety of methods used  in multidimensional data visualization. The emphasis is placed on new research results and trends in this field, including optimization, artificial neural networks, combinations of algorithms, parallel computing, different proximity measures, nonlinear manifold learning,  and more. Many of the applications presented allow us to discover the obvious advantages of visual data mining—it is much easier for a decision maker to detect or extract useful information from graphical representation of data than from raw numbers.

The fundamental idea of visualization is to provide data in some visual form that lets humans  understand them, gain insight into the data, draw conclusions, and directly influence the process of decision making. Visual data mining is a field where human participation is integrated in the data analysis process; it covers data visualization and graphical presentation of information.

Multidimensional Data Visualization is intended for scientists and researchers in any field of study where complex and multidimensional data must be visually represented. It may also serve as a useful research supplement for PhD students in operations research, computer science, various fields of engineering,  as well as natural and social sciences.

Keywords

Zilinskas

Authors and affiliations

  • Gintautas Dzemyda
    • 1
  • Olga Kurasova
    • 2
  • Julius Žilinskas
    • 3
  1. 1.Institute of Mathematics & InformaticsVilnius UniversityVilniusLithuania
  2. 2.Institute of Mathematics & InformaticsVilnius UniversityVilniusLithuania
  3. 3.Recognition Processes DepartmentVilnius UniversityVilniusLithuania

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-0236-8
  • Copyright Information Springer Science+Business Media, LLC 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4419-0235-1
  • Online ISBN 978-1-4419-0236-8
  • Series Print ISSN 1931-6828
  • Series Online ISSN 1931-6836
  • About this book