VIPS - Visual Interactive Parameter Steering

Theoretical (Analytical):

Practical (Implementation):

Literature Work:


The project will develop and enhance iterative algorithm visualizations with steerable parameters and investigate several strategies to involve the human in the analysis process.

Problem Statement

Understanding and parametrizing data mining or machine learning algorithms is often a challenge especially for non-computer science users. This project aims to visualize and steer state of the art algorithm iterations in order to bridge the analytics gap between humans and these algorithms.


  • Get familiar with the state of the art data mining algorithms, the current system and possible application areas
  • Design and implement several interactive algorithm visualizations
  • Re-implement data mining algorithms if necessary


  • Basic knowledge about STAR data mining algorithms and visual analytics
  • Advanced conceptual skills (software architectures)
  • Good programming skills in Javascript and SVN/GIT.


  • Scope: Bachelor/Master
  • 6 Month Project
  • 6 Month Thesis



  • Data mining and machine learning algorithms, visual analytics
  • Dominik Sacha, Andreas Stoffel, Florian Stoffel, Bum Kwon, Geoffrey Ellis, Daniel Keim, “Knowledge Generation Model for Visual Analytics”, IEEE Transactions on Visualization & Computer Graphics, no. 1, pp. 1, PrePrints PrePrints, doi:10.1109/TVCG.2014.2346481