Visual Data Mining:Theory, Techniques and Tools for Visual Analytics
Series: Lecture Notes in Computer Science
Subseries: Information Systems and Applications, incl. Internet/Web, and HCI , Vol. 4404
Simoff, Simeon; Böhlen, Michael H.; Mazeika, Arturas (Eds.)


2008, X, 407 p., Softcover
ISBN: 978-3-540-71079-0

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.

Keywords:

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