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Visual Data Mining

Theory, Techniques and Tools for Visual Analytics

  • Book
  • © 2008

Overview

Editors:
  1. Simeon J. Simoff
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  2. Michael H. Böhlen
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  3. Arturas Mazeika
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 4404)

Part of the book sub series: Information Systems and Applications, incl. Internet/Web, and HCI (LNISA)

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About this book

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.

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Table of contents (22 chapters)

  1. Front Matter

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  2. Visual Data Mining: An Introduction and Overview

    1. Visual Data Mining: An Introduction and Overview

      • Simeon J. Simoff, Michael H. Böhlen, Arturas Mazeika
      Pages 1-12

  3. Part 1 – Theory and Methodologies

    1. The 3DVDM Approach: A Case Study with Clickstream Data

      • Michael H. Böhlen, Linas Bukauskas, Arturas Mazeika, Peer Mylov
      Pages 13-29

    2. Form-Semantics-Function – A Framework for Designing Visual Data Representations for Visual Data Mining

      • Simeon J. Simoff
      Pages 30-45

    3. A Methodology for Exploring Association Models

      • Alipio Jorge, João Poças, Paulo J. Azevedo
      Pages 46-59

    4. Visual Exploration of Frequent Itemsets and Association Rules

      • Li Yang
      Pages 60-75

    5. Visual Analytics: Scope and Challenges

      • Daniel A. Keim, Florian Mansmann, Jörn Schneidewind, Jim Thomas, Hartmut Ziegler
      Pages 76-90

  4. Part 2 – Techniques

    1. Using Nested Surfaces for Visual Detection of Structures in Databases

      • Arturas Mazeika, Michael H. Böhlen, Peer Mylov
      Pages 91-102

    2. Visual Mining of Association Rules

      • Dario Bruzzese, Cristina Davino
      Pages 103-122

    3. Interactive Decision Tree Construction for Interval and Taxonomical Data

      • François Poulet, Thanh-Nghi Do
      Pages 123-135

    4. Visual Methods for Examining SVM Classifiers

      • Doina Caragea, Dianne Cook, Hadley Wickham, Vasant Honavar
      Pages 136-153

    5. Text Visualization for Visual Text Analytics

      • John Risch, Anne Kao, Stephen R. Poteet, Y. -J. Jason Wu
      Pages 154-171

    6. Visual Discovery of Network Patterns of Interaction between Attributes

      • Simeon J. Simoff, John Galloway
      Pages 172-195

    7. Mining Patterns for Visual Interpretation in a Multiple-Views Environment

      • José F. Rodrigues Jr., Agma J. M. Traina, Caetano Traina Jr.
      Pages 196-214

    8. Using 2D Hierarchical Heavy Hitters to Investigate Binary Relationships

      • Daniel Trivellato, Arturas Mazeika, Michael H. Böhlen
      Pages 215-235

    9. Complementing Visual Data Mining with the Sound Dimension: Sonification of Time Dependent Data

      • Monique Noirhomme-Fraiture, Olivier Schöller, Christophe Demoulin, Simeon J. Simoff
      Pages 236-247

    10. Context Visualization for Visual Data Mining

      • Mao Lin Huang, Quang Vinh Nguyen
      Pages 248-263

    11. Assisting Human Cognition in Visual Data Mining

      • Simeon J. Simoff, Michael H. Böhlen, Arturas Mazeika
      Pages 264-280

  5. Part 3 – Tools and Applications

    1. Immersive Visual Data Mining: The 3DVDM Approach

      • Henrik R. Nagel, Erik Granum, Søren Bovbjerg, Michael Vittrup
      Pages 281-311

    2. DataJewel: Integrating Visualization with Temporal Data Mining

      • Mihael Ankerst, Anne Kao, Rodney Tjoelker, Changzhou Wang
      Pages 312-330
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