Visual Data Mining: Theory, Techniques and Tools for Visual Analytics (Lecture Notes in Computer Science)

Annotation 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 Front Matter....Pages - Visual Data Mining: An Introduction and Overview....Pages 1-12 The 3DVDM Approach: A Case Study with Clickstream Data....Pages 13-29 Form-Semantics-Function – A Framework for Designing Visual Data Representations for Visual Data Mining....Pages 30-45 A Methodology for Exploring Association Models....Pages 46-59 Visual Exploration of Frequent Itemsets and Association Rules....Pages 60-75 Visual Analytics: Scope and Challenges....Pages 76-90 Using Nested Surfaces for Visual Detection of Structures in Databases....Pages 91-102 Visual Mining of Association Rules....Pages 103-122 Interactive Decision Tree Construction for Interval and Taxonomical Data....Pages 123-135 Visual Methods for Examining SVM Classifiers....Pages 136-153 Text Visualization for Visual Text Analytics....Pages 154-171 Visual Discovery of Network Patterns of Interaction between Attributes....Pages 172-195 Mining Patterns for Visual Interpretation in a Multiple-Views Environment....Pages 196-214 Using 2D Hierarchical Heavy Hitters to Investigate Binary Relationships....Pages 215-235 Complementing Visual Data Mining with the Sound Dimension: Sonification of Time Dependent Data....Pages 236-247 Context Visualization for Visual Data Mining....Pages 248-263 Assisting Human Cognition in Visual Data Mining....Pages 264-280 Immersive Visual Data Mining: The 3DVDM Approach....Pages 281-311 DataJewel: Integrating Visualization with Temporal Data Mining....Pages 312-330 A Visual Data Mining Environment....Pages 331-366 Integrative Visual Data Mining of Biomedical Data: Investigating Cases in Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia....Pages 367-388 Towards Effective Visual Data Mining with Cooperative Approaches....Pages 389-406 Back Matter....Pages - Simeon J. Simoff, Michael H. Böhlen, Arturas Mazeika (eds.). Includes Bibliographical References And Index. Also Available Via The Internet.