Data Mining - Interactive curriculae of TU Ilmenau
The interactive curriculae provide information on the degree programmes offered by the TU Ilmenau.
Please refer to the respective study and examination rules and regulations for the legally binding curricula (Annex Curriculum).
You can find all details on planned lectures and classes in the course catalogue.
Please note that this page is no longer updated. All modules and study plans from PO version 2021 onwards (Bachelor and Master study programs) are now available on the Campus Portal.
| module properties module number 6248 - common information | |
|---|---|
| module number | 6248 |
| department | Department of Economic Sciences and Media |
| ID of group | 2532 (Quantitative Methods in Economics) |
| module leader | Prof. Dr. Udo Bankhofer |
| language | Deutsch |
| term | Wintersemester |
| previous knowledge and experience | Bachelorabschluss |
| learning outcome | Die Studierenden sind in der Lage, große Datenmengen zu analysieren und entsprechende Methoden bei der Auswertung dieser Daten richtig einzusetzen. Sie können die Analyseergebnisse bewerten und im Hinblick auf die zugrundeliegende Problemstellung interpretieren. Mit der Vorlesung und der Übung werden Fach- und Methodenkompetenz vermittelt. |
| content | 1. Data Warehouse und KDD 2. Methoden und Anwendungsbereiche 3. Assoziationsanalyse 3.1 Grundlagen 3.2 Generierung häufiger Itemmengen 3.3 Generierung von Assoziationsregeln 3.4 Interessantheitsmaße 5. Segmentierungsverfahren 6. Entscheidungsbaumverfahren |
| media of instruction and technical requirements for education and examination in case of online participation | Interaktives Tafelbild, PowerPoint-Folien |
| literature / references | Jeweils in der aktuellen Auflage: Berry , M.; Linoff, G.: Mastering data mining, Wiley Fayyad, U.M. et al. (Hrsg.): Advances in Knowledge Discovery and Data Mining. Menlo Park Hippner, H. et al.: Handbuch Data Mining im Marketing, Vieweg Küppers, B.: Data Mining in der Praxis. Lang, Frankfurt Lusti, M.: Data warehousing und data mining, Springer |
| evaluation of teaching | |
| Details reference subject | |
|---|---|
| module name | Data Mining |
| examination number | 2500102 |
| credit points | 4 |
| SWS | 2 |
| on-campus program (h) | 22.5 |
| self-study (h) | 97.5 |
| obligation | obligatory module |
| exam | written examination performance, 60 minutes |
| details of the certificate | |
| link to Moodle course | |
| teacher | |
| signup details for alternative examinations | |
| maximum number of participants | |
|
Details
in degree program
Master Wirtschaftsingenieurwesen 2009, Master Wirtschaftsingenieurwesen 2010 ATTENTION: not offered anymore |
|
|---|---|
| module name | Data Mining |
| examination number | 2500102 |
| credit points | 3 |
| on-campus program (h) | 22 |
| self-study (h) | 68 |
| obligation | elective module |
| exam | written examination performance, 60 minutes |
| details of the certificate | |
| link to Moodle course | |
| signup details for alternative examinations | |
| maximum number of participants | |