Visual Data Mining: Theory, Techniques and Tools for Visual Analytics / Edition 1

Visual Data Mining: An Introduction and Overview.- Visual Data Mining: An Introduction and Overview.- 1 – Theory and Methodologies.- The 3DVDM Approach: A Case Study with Clickstream Data.- Form-Semantics-Function – A Framework for Designing Visual Data Representations for Visual Data Mining.- A Methodology for Exploring Association Models.- Visual Exploration of Frequent Itemsets and Association Rules.- Visual Analytics: Scope and Challenges.- 2 – Techniques.- Using Nested Surfaces for Visual Detection of Structures in Databases.- Visual Mining of Association Rules.- Interactive Decision Tree Construction for Interval and Taxonomical Data.- Visual Methods for Examining SVM Classifiers.- Text Visualization for Visual Text Analytics.- Visual Discovery of Network Patterns of Interaction between Attributes.- Mining Patterns for Visual Interpretation in a Multiple-Views Environment.- Using 2D Hierarchical Heavy Hitters to Investigate Binary Relationships.- Complementing Visual Data Mining with the Sound Dimension: Sonification of Time Dependent Data.- Context Visualization for Visual Data Mining.- Assisting Human Cognition in Visual Data Mining.- 3 – Tools and Applications.- Immersive Visual Data Mining: The 3DVDM Approach.- DataJewel: Integrating Visualization with Temporal Data Mining.- A Visual Data Mining Environment.- Integrative Visual Data Mining of Biomedical Data: Investigating Cases in Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia.- Towards Effective Visual Data Mining with Cooperative Approaches.