Inhalt

[ 926BUSIDAMU14 ] UE Data Mining

Versionsauswahl
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Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M1 - Master's programme 1. year Business Informatics Felix Burgstaller 2 hpw Johannes Kepler University Linz
Detailed information
Pre-requisites (*)keine
Original study plan Master's programme Business Informatics 2022W
Objectives See Data Mining module
Subject See Data Mining module
Criteria for evaluation Practical exercises, presentation of case studies based on the lecture material
Methods Students work in small groups to solve practical problems using the knowledge imparted in the lecture and exercise; presentation, discussion and documentation of the respective work results.
Language German/English
Study material Literature:

  • Han, J.; Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, current edition.

Supplemental Literature:

  • Witten, I. H.; Hall, M.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, current edition.
  • Kotu, V.; Deshpande, B.: Predictive Analytics and Data Mining: Concepts and Practice with Rapidminer. Morgan Kaufmann, current edition.
  • Van der Aalst, W.: Process Mining. Springer, current edition.
  • Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer Verlag, current edition.

Other supplemental literature will be announced each semester.

Changing subject? No
On-site course
Maximum number of participants 30
Assignment procedure Assignment according to priority