[ 921DASICDAK17 ] KV Computational Data Analytics

Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M1 - Master's programme 1. year Computer Science Johannes F├╝rnkranz 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Computer Science 2019W
Objectives This course offers a basic introduction to data mining and knowledge discovery, both from a theoretical and practical point of view.
Subject Topics covered include

  • Data mining process models
  • Pre-processing techniques
  • Inductive rule learning
  • Efficient similarity-based techniques
  • Association rule mining
  • Graph Mining
  • Evaluation
Criteria for evaluation Exam, Project assignment
Methods Slide Presentations with Practical Exercises
Language (*)English
Study material I. H. Witten, E. Frank, M. A. Hall, C. J. Pal: Data Mining. Morgan Kaufmann.
J. Leskovec, A. Rajaraman, J. D. Ullman: Mining of Massive Datasets. Cambridge University Press.
Changing subject? No
On-site course
Maximum number of participants -
Assignment procedure Direct assignment