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| Detailed information |
| Pre-requisites |
(*)keine
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| 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 |
| Earlier variants |
They also cover the requirements of the curriculum (from - to)
2WBMDMU: UE Data Mining (2011S-2014S)
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