- 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
|Theoretical (Analytical):||(4 / 5)|
|Practical (Implementation):||(4 / 5)|
|Literature Work:||(4 / 5)|
The main aim of this project is to implement an exploitative visual analytics system for analyzing the parameter space of topic models.
Topic models are algorithms that attempt to extract the thematic structure of text corpora.
The goal of this project is to visualize the parameter space of topic modeling algorithms in order ensure a higher understandability and trustworthiness of these techniques. This project is motivated from the need of humanities scholars to understand the data mining algorithms they use for their analysis to make better choices and justify their inferences from the analysis. Using visual analytics, we can open the “black-box” of such approaches and enhance the task of understanding the algorithms and their parameters.
- Review existing tools and approaches.
- Make yourself familiar with the data.
- Implement feature extraction techniques suitable for the analysis tasks.
- Develop a visualization prototype to explore these features interactively.
- Evaluate your approach.
- Basic knowledge about information visualization and natural language processing.
- Scope: Master
- 6 Months Project, 6 Months Thesis