rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Multidimensional Data Visualization
    sub title: Methods and Applications
  name:
    name part: Dzemyda, Gintautas.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Kurasova, Olga.
    role:
      role term: author.
  name:
    name part: Žilinskas, Julius.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XII, 252 p. online resource.
  abstract: 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.
  table of contents: Preface -- 1. Multidimensional Data and the Concept of Visualization -- 2. Strategies for Multidimensional Data Visualization -- 3. Optimization-Based Visualization -- 4. Combining Multidimensional Scaling with Artificial Neural Networks -- 5. Applications of Visualizations -- A. Test Data Sets -- References -- Index.
  note: by Gintautas Dzemyda, Olga Kurasova, Julius Žilinskas.
  subject:
    topic: Mathematics
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer simulation
  subject:
    topic: Visualization
  subject:
    topic: Mathematical optimization
  subject:
    topic: Mathematics
  subject:
    topic: Optimization
  subject:
    topic: Simulation and Modeling
  subject:
    topic: Visualization
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: QA402.5-402.6
  classification: 519.6
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Optimization and Its Applications, 75
  identifier: 9781441902368
  identifier: http://dx.doi.org/10.1007/978-1-4419-0236-8
  record info:
    record creation date: 121116
    record change date: 20141215021755.0
    record identifier: 978-1-4419-0236-8