The project will develop a client-server architecture for clustering computations (backend) with a web-based visual interface for clustering workflows. It is inspired by an existing approach for cluster analysis using self-organizing maps.
Iterative refinement and filter steps are needed to successively approach a desired visualization of a certain dataset. However, a variety of different clustering algorithms exists. This project aims to embed different algorithms into a common clustering workflow approach that allows the user to iteratively switch and compare different results.
- Get familiar with the state of the art data mining and machine learning algorithms and the current system.
- Design and implement several interactive algorithm visualizations and embed them in a workflow.
- Basic knowledge about machine learning, data mining algorithms and visual analytics
- Advanced programming skills in Java
- Good conceptual skills (software architectures)
- Basic knowledge in REST, Client-Server communication, Ajax/Jersey, JSON
- Useful: Git, Maven
- Scope: Master
- 6 Month Project
- 6 Month Thesis
- D. Sacha and M. Kraus and J. Bernard and M. Behrisch and T. Schreck and Y. Asano and D. A. Keim:
SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance. IEEE Transactions on Visualization and Computer Graphics (2017)
- Dominik Sacha, Michael Sedlmair, Leishi Zhang, John Aldo Lee, Jaakko Peltonen, Daniel Weiskopf, Stephen C. North, Daniel A. Keim:
What you see is what you can change: Human-centered machine learning by interactive visualization. Neurocomputing 268: 164-175 (2017)