- 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
Overview and Problem Statement
Which is an appropriate dataset to evaluate a visualization for high-dimensional data?
In visualization research, quite often an arbitrary dataset is used to show the application and usefulness of a novel technique. These datasets typically differ significantly in their data distributions, dimensionality and the number of data records. Therefore, a fair comparison to competetive approaches is often not possible as data characteristics, problems and user tasks differ.
The goal of this project is to give an overview about the most commonly used datasets, their applications and to give researchers guidance for the selection of an appropriate dataset for given analysis tasks.
- Literature review to extract commonly used datasets together with their application
- Development of an interactive website to explore and visualize the datasets
- Development of meta-visualizations of the used datasets
- Good knowledge in information visualization
- Motivation to search for and read many scientific papers about high-dimensional visualizations
- Scope: Bachelor/Master
- 6 Month Project, 3 Month Thesis (Bachelor) / 6 Month Thesis (Master)
- Start: immediately
|Theoretical (Analytical):||(2 / 5)|
|Practical (Implementation):||(4 / 5)|
|Literature Work:||(5 / 5)|
- Generative Data Models for Validation and Evaluation of Visualization Techniques [Schulz et al., 2016]
- Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA:
University of California, School of Information and Computer Science.
- SOURCESIGHT: Enabling Effective Source Selection [Rekatsinas, Theodoros, et al., 2016]
- An Interactive Data Repository with Visual Analytics [Rossi and Ahmed, 2016]