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| Detailed information |
| Pre-requisites |
(*)keine
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| Original study plan |
Master's programme Business Informatics 2025W |
| Learning Outcomes |
Competences |
| See lecture of the same name.
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| Skills |
Knowledge |
| See lecture of the same name.
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See lecture of the same name.
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| Criteria for evaluation |
Written exam (midterm and final exam)
Practical exercises, presentation of case studies based on the lecture material
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| 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.
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| 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.
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| Changing subject? |
No |
| Further information |
VL and UE Data Mining form an inseparable didactic unit. The learning outcomes described are achieved through the interaction of both.
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| Earlier variants |
They also cover the requirements of the curriculum (from - to)
2WBMDMU: UE Data Mining (2011S-2014S)
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