Visual Data Mining: Theory, Techniques and Tools for Visual Analytics

Simeon J. Simoff, Michael H. Böhlen, Arturas Mazeika (auth.), Simeon J. Simoff, Michael H. Böhlen, Arturas Mazeika (eds.)9783540710790, 3-540-71079-5

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.

Table of contents :
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 –