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Abstract
Proximity Visualization of Abstract Data
Wojciech Basalaj
January 2001
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Abstract
Introduction
What is Proximity Visualization
Data Types
Quantitative
Ordinal
Nominal
Binary
Heterogeneous
Relationships
Images
Text Corpus
Proximity
Dissertation Aims
Evaluation Framework
Multivariate Visualization Techniques
Running Example
Parallel Coordinates
Andrews Plot
Multidimensional Scaling
Scatterplot Matrix
Iconographic Displays
Star Glyphs
Chernoff Faces
Summary
Multidimensional Scaling
Problem Definition
Loss Functions
Raw Stress
Normalised Stress
Kruskal's Stress
Energy
Algorithms
Classical Scaling
Newton-Raphson
Tabu Search
Genetic Algorithm
Majorization Algorithm
Simulated Annealing
Experimental Setup
Statistical Analysis
Minimum Energy
Average Energy
Running Time
Identifying the Best Algorithm
Hybrid Algorithm
Qualitative Evaluation
Discussion
Related Work
Large Scale Visualization
Challenges of Visualizing Large Data Collections
Incremental Multidimensional Scaling
Single-link Clustering
The Algorithm Outline
Time Complexity
Space Complexity
Empirical Characteristics
Principal Components Analysis
Comparison
Statistical Analysis
Two-dimensional Visualization
Three-dimensional Visualization
Running Time
Qualitative Evaluation
Discussion
Related Work
Proximity Grid
Origins of the Problem
Algorithms
Greedy
Improved Greedy
``Squeaky Wheel'' Optimization
Genetic Algorithm
Comparison
Statistical Analysis
Allocation Strategy Analysis
Algorithm Analysis
Grid Density Analysis
Complete Analysis
Qualitative Evaluation
Discussion
Related Work
Case Studies
Protein Interactions
Image Browsing
Databases
Metadata Visualization
Data Visualization
Conclusions
Partial Derivatives of Energy
The First Partial Derivative of the Euclidean Distance
The First Partial Derivative of Energy
Second Partial Derivatives of Energy
Test Bed
Small Data Sets
Medium Data Sets
Large Data Sets
Bibliography
© 2001
Wojciech Basalaj