On September 30h, 2024 the Data Science Initiative organized a symposium on Data Mining.

We focused on on data mining, this includes unsupervised learning, pattern extraction from (big) data, hypothesis generation using exploratory data analysis, finding groups in data, anomaly detection, ... .

Program

1st Session 14:00 – 15:15 

  • Reuma Arav (Institute of  Geomatics): Deep learning of topographic anomalies for the detection of regions of interest in 3D point clouds 
  • Aleš Kuchař (Institute of Meteorology and Climatology): Unsupervised Deep Learning for Atmospheric Pattern Detection Using Convolutional Autoencoders
  • Lena Ortega-Menjivar (Institute of Statistics): Finding Austrian domestic food waste producers under challenging data conditions - Clustering of ordinal survey data with missing values 

Coffee Break 15:15 – 15:45 

2nd Session 15:45 – 17:30 

  • Emma Izquierdo-Verdiguier (Institute of  Geomatics): Discovering Hidden Earth Surface
  • Vlad Surdea-Hernea (Institute of Forest, Environmental and Natural Resource Policy): LLM-based analysis of 'Pro-Environmentalism' in EU Electoral Programs 
  • Johannes Stadlmann (Institute of Biochemistry): Analysis workflow to identify complex carbohydrate (i.e. glycan) structures from rather large LC-MS/MS data sets

Discussion and Networking 17:30 – open end

For more information (mailing list, forums, contact), please visit: https://short.boku.ac.at/datascience 

Cute fuzzy monsters putting rectangular data tables onto a conveyor belt. Along the conveyor belt line are different automated “stations” that update the data, reading “WRANGLE”, “VISUALIZE”, and “MODEL”. A monster at the end of the conveyor belt is carrying away a table that reads “Complete analysis.”