Machine Learning Methods in Visualisation for Big Data 2018

Tutorial co-located with EuroVis 2018, June 4, 2018, Brno, Czech Republic

Programme

  • 9:05-9:15 Introduction
  • 9:15-9:45 Dimensionality Reduction for Visualization (Presenter: Jaakko Peltonen)
    • Component analysis methods
    • Self-organizing maps
    • Multidimensional Scaling (MDS) and its variants
    • Methods that preserve similarities or neighbourhood relationships

    Slides in PDF format

  • 9:45-10:15 Sampling Methods and Graph Visualisation (Presenter: Daniel Archambault)
    • Influence of sampling on perception of graph structure
    • Sampling across time on dynamic graphs.

    Slides in PDF format

  • 10:15-10:40 Paper presentation: “Panning for Insight: Amplifying Insight through Tight Integration of Machine Learning, Data Mining, and Visualization”, Benjamin Karer, Inga Scheler and Hans Hagen, TU Kaiserslautern, Germany
    Slides in PDF format (note: full paper available as part of the Eurovis 2018 co-located event proceedings)

  • 10:40-11:10 Coffee Break
  • 11:10-11:45 Evaluating Visualisation Techniques (Presenter: Ian Nabney)
    • Why is evaluation important yet difficult?
    • User-based evaluation: perceptual evaluation, study design
    • Metric-based evaluation: model-based metrics, unsupervised learning metrics, task-based metrics

    Slides in PDF format

  • 11:45-12:15 Panel Discussion
    Tentative topics:

    • Future fruitful lines of research
    • Obstacles/challenges to increased combination of ML and vis
    • How to evaluate ML&vis at different levels, and convince others of the evaluation?
    • How do we relate do data science?
    • Topics for the next CFP
  • 12:15-12:45 Data Lab: Bring Your Own Data
  • 12:45-12:50 Closing