- Walking Wales: the visualisation challenge
- Word Clouds: Can they be misleading?
- VIPS-Visual Interactive Parameter Steering
- Confirmation bias : do multiple views really help?
- Visual Query Interface for Infrastructure Networks
- MADucator – Matrix Educator
- Concensus Matrix Sort
- Multiscale Multiples: An Overview+Detail Interface for Small Multiples
- Analyzing Image Similarity to Detect Disguised Plagiarism
- Tackling the Multi-Analyst Problem in Soccer: How to Improve Collaborative Pattern Detection in Team Sports
- MDSQ – Quality Assessment of Distance-Preserving Projections
- Internal Quality Measures for Subspace Clusterings
- Exploration of Datasets for Visualizations of High-Dimensional Data
- Search and Visual Exploration of Scientific Literature
- Star Glyph – Optimal Dimension Layout
- Visual Analytics of Molecular Neurochemicals and Biological Markers in Mental Illness
- Ensembles of classifiers: Visual construction of classification models
- Cutting-Down the Complexity of Parallel Coordinates Plots in High-Dimensional Data
- Investigating Analytic Behavior in VA
- Hierarchical Matrix Visualization
- Realisierung und Evaluierung einer stereoskopischen 3D Perspective Wall Umgebung für verlinkte Informationsvisualisierung (Master)
- Combination of Matrices + Graphs
- TreeMap Evaluation
- Visual Analysis of Language Change over Time
- Visual Parameter Space Analysis of Topic Models
Adapted from: Johansson & Johansson. “Interactive dimensionality reduction through user-defined combinations of quality metrics.” Visualization and Computer Graphics, 2009.
Overview and Problem Statement
Die usefulness and qualtiy of a parallel coordinates plot is highly influenced by the ordering of the dimensions. Putting correlated or similar dimensions next to each other, enables the user to identify interesting patterns, while noisy dimensions between patterns are counter-productive.
In datasets with a large number of dimensions, there is often not ‘one best ordering’ of the dimensions, but rather different useful orderings. Furthermore, some dimensions are not relevant for specific analysis tasks and make reading a visualization more complex.
The aim of this project is to optimize parallel coordinates plots by identifying relevant combinations of dimensions and searching for a useful orderings which highlight patterns for high-dimensional data.
- Implement an algorithm which extracts subsets of dimensions for a specific task.
- Optimize the ordering of these dimensions in a parallel coordinates plot.
- Good knowledge in information visualization
- Scope: Bachelor/Master
- 6 Month Project, 3 Month Thesis (Bachelor) / 6 Month Thesis (Master)
- Start: immediately
|Theoretical (Analytical):||(3 / 5)|
|Practical (Implementation):||(5 / 5)|
|Literature Work:||(2 / 5)|
- State of the art of parallel coordinates [Heinrich and Weiskopf, 2013]
- Pargnostics: Screen-space metrics for parallel coordinates [Dasgupta and Kosara, 2010]
- Visualizing High-Dimensional Structures by Dimension Ordering and Filtering using Subspace Analysis [Ferdosi and Roerdink, 2011]