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

✍ Scribed by Arturas Mazeika Simeon J. (EDT) Simoff Simeon J. Simoff,Simeon Simoff,Michael H. B. Hlen

• Tongue: English
• Leaves: 417
• Category: Library

✦ Table of Contents

Title Page
Foreword
Preface
Table of Contents
Visual Data Mining: An Introduction and Overview
Introduction
The Term
The Process
The Book
Conclusions and Acknowledgements
References
The 3DVDM Approach: A Case Study with Clickstream Data
Introduction
Clickstreams
Web Server Log Files
Modeling Clickstreams in a DataWarehouse
Analyzing Clickstreams
The 3DVDM System
Overall Architecture
Streaming Visible Data
Model Computation
Data Analyzes and Interpretation
Animation
Conditional Density Surfaces
Equalization of Structures
Windowing
Summary and Future Work
References
Form-Semantics-Function – A Framework for Designing Visual Data Representations for Visual Data Mining
Introduction
Form-Semantics-Function: A Formal Approach Towards Constructing and Evaluating Visualisation Techniques
Metaphor Analysis
Metaphor Formalisation
Metaphor Evaluation
Constructing Visualisation Schema for Visual Data Mining for Identifying Patterns in Team Collaboration
Metaphor Analysis
Metaphor Formalisation
Evaluation and Comparison of Two Visualisation Schemata
Metaphor Evaluation
Conclusion and Future Directions
References
A Methodology for Exploring Association Models
Introduction
Association Rules
The Association Rule Space
PEAR: A Web-Based AR Browser
Chunking Large Sets of Rules
Global Metrics for Sets of Rules
The Index Page
Operators for Sets of Association Rules
Example of the Application of the Proposed Methodology
Implementation
Scalable Vector Graphics
Representing Associations Rules with PMML
Performance
Related Work
Future Work and Conclusions
References
Visual Exploration of Frequent Itemsets and Association Rules
Introduction
BasicConcepts
Itemset Lattice and Closure Properties
Parallel Coordinates
Dealing with Item Taxonomy
Experiments and Screen Snapshots
Visualization of Iceberg Data Cubes
Related Work
Conclusion and Future Work
References
Visual Analytics: Scope and Challenges
Introduction
Scope of Visual Analytics
Visual Analytics Process
Application Challenges
Physics and Astronomy
Business
Environmental Monitoring
Disaster and Emergency Management
Security
Software Analytics
Biology, Medicine and Health
Engineering Analytics
Personal Information Management
Mobile Graphics and Traffic
Technical Challenges
Conclusion
References
Using Nested Surfaces for Visual Detection of Structures in Databases
Introduction
Motivation
Preliminaries
Probability Density Function
Clusters and Outliers
Surface Definition
Algorithms
Evaluation
Quality of the Surfaces
Space and Time Complexities
Experiments
Summary and Future Work
References
Visual Mining of Association Rules
Introduction
Some Issues about Association Rules
A Real Data Set Application
Visualizing Association Rules
Rule Table
Two-Dimensional Matrix
3-D Visualization
Association Rules Networks
The TwoKey Plot
Double-Decker Plot
Parallel Coordinates
Factorial Planes
Concluding Remarks
References
Interactive Decision Tree Construction for Interval and Taxonomical Data
Introduction
Interactive Decision Tree Construction
Interval Data
Ordering Interval Data
Graphical Representation of Interval Data
Classifying Interval Data with PBC
Classifying Interval Data with CIAD
Interval SVM Algorithm
Taxonomical Data
Graphical Representation of a Taxonomical Variable
Interactive Taxonomical Data Classification
Some Results
Conclusion and Future Work
References
Visual Methods for Examining SVM Classifiers
Introduction
Methods
Support Vector Machines
Tours Methods for Visualization
SVM and Tours
Application
Data Description
Visualizing SVM Outputs
Gene Selection
Varying SVM Input Parameters
Model Stability
Summary and Discussion
Summary
Discussion
References
Text Visualization for Visual Text Analytics
Introduction
Overview of Visual Text Analytics Technology
Functional Components of Visual Text Analytics Systems
Tokenization
Vector Representation
Dimensionality Reduction
Spatialization
Labeling
Interactive Exploration
Summary
Example Systems
Sammon
Lin
Bead
IN-SPIRE
WEBSOM
Starlight
References
Visual Discovery of Network Patterns of Interaction between Attributes
Introduction
Modeling Perspectives in Analytics
Network Models in Analytics
The “Loss of Detail” Problem in Data Mining
The “Independency of Attributes” Assumption in Data Mining
Visual Discovery of Network Patterns of Interaction between Attributes
The Approach and the Processes
Case Studies
Case A: Fraud Detection in Insurance Industry
Case B: Visual Discovery in Internet Traffic Analysis
Conclusion
References
Mining Patterns for Visual Interpretation in a Multiple-Views Environment
Introduction
Related Work
Multiple-Views within the Visualization Tree
Visualization Pipeline
Visualization Composition
The System
Features of the VisTree Methodology
Exploration Techniques
Frequency Plot
Relevance Plot
Representative Plot
Experiments
Conclusions
References
Using 2D Hierarchical Heavy Hitters to Investigate Binary Relationships
Introduction
Related Work
Two-Dimensional Hierarchical Heavy Hitters
Filtering
Grouping 1D Hierarchical Data
Grouping 2D Hierarchical Data
Computation of Counts in a Lattice
VHHH: Visualization of Hierarchical Heavy Hitters
Visualization of the Lattice and HHH Information
Ordering of Categorical Data
Experiments
Case Studies with Real World Data
VHHH Alphabet
Pattern Investigation of VHHH
HHH Ordering Versus Dataset Ordering
Conclusions and Future Work
References
Complementing Visual Data Mining with the Sound Dimension: Sonification of Time Dependent Data
Introduction
Characteristics of Sound for Time Dependent Data Representation
Sonification
Detection of Outliers
Beat Drums Mapping
Stereo Panning
3D Curve
Experimental Workbench for Data Sonification and Mining
Design of the Experiment and Methodology of Data Collection
Results of the Experiments
The Sample of Participants
Results in 2D
Results in 3D
Discussion
Conclusions
References
Context Visualization for Visual Data Mining
Introduction
Formal Model of Interactive Visualization for Visual Data Mining
Interactive Navigation in Information Visualization
Visual Exploration in Visual Data Mining
The Concept of Visual Exploration with a Chain of Context Views
Context Visualization with a Chain of Context Views
History Visualization for Visual Data Mining
Conclusion
References
Assisting Human Cognition in Visual Data Mining
Introduction
Visual Bias in Visual Data Mining
Addressing the Visual Bias in Visual Data Mining
The Method of Guided Cognition, Implemented through Embedded Statistical Techniques
The Method of Validated Cognition, Implemented through a Combination of Visual Data Mining Techniques
Visual Analysis
Validation
Summary and Future Directions
References
Immersive Visual Data Mining: The 3DVDM Approach
Introduction
Virtual Reality
Immersive Visual Data Mining
Exploiting Sound
Previous Work
Visual Data Exploration
Auditory Data Exploration
Immersive vs. Traditional VDM
The3DVDMSystem
VR++ and 3DVDM
Data Pipeline and Interaction
Principles of Data Visualization in 3DVDM
Rendering Sound
BasicTools
Visual Data Exploration
Auditory Data Exploration
Methodology for Visual Data Exploration in 3D Worlds
Data Preparation
Basic Statistical Analysis
Visual Exploration of Static Worlds
3D Scatter Plot Tour
3D Scatter Plots and Object Properties
Visual Exploration of Dynamic Worlds
Macro Dynamic Visualization
Micro Dynamic Visualization
Auditory Exploration of Static Worlds
Sound Supporting Color
Sound “On Its Own” – Categorical Variables
Sound “On Its Own” – Continuous Variables
Discussion
Visual Data Exploration
Auditory Data Exploration
Conclusions
References
DataJewel: Integrating Visualization with Temporal Data Mining
Introduction
Related Work
User-Centric Data Mining
The Visualization Component
CalendarView
Interaction with CalendarView
The Temporal Mining Component
The Database Component
Experiments
Mining Airplane Maintenance Datasets
The DataJewel System
Discussion
Conclusions
References
A Visual Data Mining Environment
Introduction
Related Work
System Architecture and Implementation
System Architecture
System Implementation
The User Interface
Identifying Regions with Good Sales: Using the Clustering Environment
Establishing Data Relationships: Using the Metaquery Environment
Market-Basket Analysis: Using the Association Rule Environment
Visual Exploration Using DARE
Formal Specification of the Visual Interface
Clustering
Usability
Heuristic Evaluation
Mock-Up Experiment
User Tests
Future Work and Conclusions
References
Integrative Visual Data Mining of Biomedical Data: Investigating Cases in Chronic Fatigue Syndrome and Acute Lymphoblastic Leukaemia
Introduction
The “Extract-Explain-Generate” Methodology
Case Study 1: Chronic Fatigue Syndrome
Problem
The Goals of the Study
The Study Scenario
The Outcomes
Case Study 2: Acute Lymphoblastic Leukaemia
Problem
The Goals of the Study
The Study Scenario
The Outcomes
Discussion and Conclusions
References
Towards Effective Visual Data Mining with Cooperative Approaches
Introduction
Interactive Decision Tree Construction
CIAD
CIAD+
Some Results of Interactive Decision Tree Algorithms
Visualization of SVM Results
Visualization of the SVM Separating Plane
Visualization of the Data Distribution According to the Distance to the Boundary
Visualization to Tune SVM Input Parameters
Cooperative Approaches for Large Datasets
Interactive SVM Construction
Cooperative Approach for Datasets with Large Number of Datapoints
Cooperative Approach for Datasets with Large Number of Dimensions
Conclusion and Future Work
References
Author Index