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Visual Data Mining 2008
- Simeon J. Simoff, Michael H. Böhlen, Arturas Mazeika:
Visual Data Mining - Theory, Techniques and Tools for Visual Analytics. Lecture Notes in Computer Science 4404, Springer 2008, ISBN 978-3-540-71079-0 - Simeon J. Simoff, Michael H. Böhlen
, Arturas Mazeika:
Visual Data Mining: An Introduction and Overview. 1-12
Theory and Methodologies
- Michael H. Böhlen
The 3DVDM Approach: A Case Study with Clickstream Data. 13-29 - Simeon J. Simoff:
Form-Semantics-Function - A Framework for Designing Visual Data Representations for Visual Data Mining. 30-45 - Alípio Jorge
A Methodology for Exploring Association Models. 46-59 - Li Yang:
Visual Exploration of Frequent Itemsets and Association Rules. 60-75 - Daniel A. Keim, Florian Mansmann, Jörn Schneidewind, James J. Thomas, Hartmut Ziegler:
Visual Analytics: Scope and Challenges. 76-90
Techniques
- Arturas Mazeika, Michael H. Böhlen
Using Nested Surfaces for Visual Detection of Structures in Databases. 91-102 - Dario Bruzzese
Visual Mining of Association Rules. 103-122 - François Poulet, Thanh-Nghi Do:
Interactive Decision Tree Construction for Interval and Taxonomical Data. 123-135 - Doina Caragea
Visual Methods for Examining SVM Classifiers. 136-153 - John Risch
Text Visualization for Visual Text Analytics. 154-171 - Simeon J. Simoff, John Galloway:
Visual Discovery of Network Patterns of Interaction between Attributes. 172-195 - José Fernando Rodrigues Jr., Agma J. M. Traina, Caetano Traina Jr.
Mining Patterns for Visual Interpretation in a Multiple-Views Environment. 196-214 - Daniel Trivellato, Arturas Mazeika, Michael H. Böhlen
Using 2D Hierarchical Heavy Hitters to Investigate Binary Relationships. 215-235 - Monique Noirhomme-Fraiture, Olivier Schöller, Christophe Demoulin, Simeon J. Simoff:
Complementing Visual Data Mining with the Sound Dimension: Sonification of Time Dependent Data. 236-247 - Mao Lin Huang
Context Visualization for Visual Data Mining. 248-263 - Simeon J. Simoff, Michael H. Böhlen
Assisting Human Cognition in Visual Data Mining. 264-280
Tools and Applications
- Henrik R. Nagel, Erik Granum, Søren Bovbjerg, Michael Vittrup:
Immersive Visual Data Mining: The 3DVDM Approach. 281-311 - Mihael Ankerst, Anne Kao, Rodney Tjoelker, Changzhou Wang:
DataJewel: Integrating Visualization with Temporal Data Mining. 312-330 - Stephen Kimani, Tiziana Catarci
A Visual Data Mining Environment. 331-366 - Paul J. Kennedy
Integrative Visual Data Mining of Biomedical Data: Investigating Cases in Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia. 367-388 - François Poulet:
Towards Effective Visual Data Mining with Cooperative Approaches. 389-406
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