[ 921DASICDAK17 ] KV Computational Data Analytics

Es ist eine neuere Version 2022W dieser LV im Curriculum Master's programme Artificial Intelligence 2023W vorhanden.
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 and French
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