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Visual Data Mining

Theory, Techniques and Tools for Visual Analytics

  • Editors
  • Simeon J. Simoff
  • Michael H. Böhlen
  • Arturas Mazeika

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4404)

Table of contents

  1. Front Matter
  2. Visual Data Mining: An Introduction and Overview

    1. Simeon J. Simoff, Michael H. Böhlen, Arturas Mazeika
      Pages 1-12
  3. Part 1 – Theory and Methodologies

    1. Michael H. Böhlen, Linas Bukauskas, Arturas Mazeika, Peer Mylov
      Pages 13-29
    2. Alipio Jorge, João Poças, Paulo J. Azevedo
      Pages 46-59
    3. Daniel A. Keim, Florian Mansmann, Jörn Schneidewind, Jim Thomas, Hartmut Ziegler
      Pages 76-90
  4. Part 2 – Techniques

    1. Arturas Mazeika, Michael H. Böhlen, Peer Mylov
      Pages 91-102
    2. Dario Bruzzese, Cristina Davino
      Pages 103-122
    3. François Poulet, Thanh-Nghi Do
      Pages 123-135
    4. Doina Caragea, Dianne Cook, Hadley Wickham, Vasant Honavar
      Pages 136-153
    5. John Risch, Anne Kao, Stephen R. Poteet, Y. -J. Jason Wu
      Pages 154-171
    6. Simeon J. Simoff, John Galloway
      Pages 172-195
    7. José F. Rodrigues Jr., Agma J. M. Traina, Caetano Traina Jr.
      Pages 196-214
    8. Daniel Trivellato, Arturas Mazeika, Michael H. Böhlen
      Pages 215-235
    9. Monique Noirhomme-Fraiture, Olivier Schöller, Christophe Demoulin, Simeon J. Simoff
      Pages 236-247
    10. Mao Lin Huang, Quang Vinh Nguyen
      Pages 248-263
    11. Simeon J. Simoff, Michael H. Böhlen, Arturas Mazeika
      Pages 264-280
  5. Part 3 – Tools and Applications

    1. Henrik R. Nagel, Erik Granum, Søren Bovbjerg, Michael Vittrup
      Pages 281-311
    2. Mihael Ankerst, Anne Kao, Rodney Tjoelker, Changzhou Wang
      Pages 312-330
    3. Stephen Kimani, Tiziana Catarci, Giuseppe Santucci
      Pages 331-366
    4. Paul Kennedy, Simeon J. Simoff, Daniel R. Catchpoole, David B. Skillicorn, Franco Ubaudi, Ahmad Al-Oqaily
      Pages 367-388
  6. Back Matter

About this book

Introduction

The importance of visual data mining, as a strong sub-discipline of data mining, had already been recognized in the beginning of the decade. In 2005 a panel of renowned individuals met to address the shortcomings and drawbacks of the current state of visual information processing. The need for a systematic and methodological development of visual analytics was detected.

This book aims at addressing this need. Through a collection of 21 contributions selected from more than 46 submissions, it offers a systematic presentation of the state of the art in the field. The volume is structured in three parts on theory and methodologies, techniques, and tools and applications.

Keywords

SVM classifiers association rules mining clusterin context visualizations data mining decision trees density surfaces multiple views pattern mining text visualizations visual analytics visual exploration visual hierarchical heavy hitters visual interpretation visualization

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-71080-6
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-71079-0
  • Online ISBN 978-3-540-71080-6
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site