Knowledge Discovery in Databases - 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Please note that this page is no longer updated.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 8232 - common information | |
|---|---|
| module number | 8232 |
| department | Department of Computer Science and Automation |
| ID of group | 2254 (Databases and Information Systems) |
| module leader | Prof. Dr. Kai-Uwe Sattler |
| language | Deutsch |
| term | Sommersemester |
| previous knowledge and experience | Vorlesungen Datenbanksysteme, Statistik |
| learning outcome | Die Studierenden verstehen nach dem Besuch dieser Veranstaltung fortgeschrittene Konzepte des Data Mining. Sie kennen den Prozess der Wissensentdeckung in Datenbanken sowie konkrete Teilaufgaben dieses Prozesses. Sie verstehen Verfahren zum Data Mining für spezielle Problemstellungen wie die Analyse von Datenströmen, raum- bzw. zeitbezogenen Daten und Graphstrukturen. Die Studierenden sind in der Lage, konkrete Data-Mining-Verfahren hinsichtlich des Einsatzes für konkrete Aufgabenstellungen auszuwählen, zu bewerten und anzuwenden. |
| content | Einführung; Grundlagen: Statistik, Daten, Datenaufbereitung; Klassische Data-Mining-Techniken: Clustering, Frequent Itemset Mining, Klassifikation; Online Mining in Datenströmen: Datenstromverarbeitung, Datenzusammenfassungen, Frequent Pattern Mining, Clustering in Datenströmen, Klassifikation; Graph Mining: Mustersuche in Graphen, Erkennen von Communities, Erkennung häufiger Subgraphen, Spatio-Temporal Mining: Sequential Pattern Mining, räumliche Ausreißer und Clustering, Prediktion; Big Data Analytics: MapReduce und Hadoop, Data-Mining-Tasks in Hadoop |
| media of instruction and technical requirements for education and examination in case of online participation | Vorlesung mit Präsentation und Tafel, Handouts, Moodle |
| literature / references | V. Kumar, M. Steinbach, P. Tan: Introduction to Data Mining, Addison Wesley, 2005. J. Han, M. Kamber, J. Pei: Data Mining: Concepts and Techniques, 3. Auflage, Morgan Kaufmann Publishers, 2011. M. Ester, J. Sander: Knowledge Discovery in Databases, Springer Verlag, 2000. |
| evaluation of teaching | |
| Details reference subject | |
|---|---|
| module name | Knowledge Discovery in Databases |
| examination number | 2200212 |
| credit points | 4 |
| SWS | 3 |
| on-campus program (h) | 33.75 |
| self-study (h) | 86.25 |
| obligation | obligatory module |
| exam | oral examination performance, 30 minutes |
| details of the certificate | |
| link to Moodle course | |
| teacher | |
| signup details for alternative examinations | |
| maximum number of participants | |
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Details
in degree program
Master Wirtschaftsinformatik 2014, Master Wirtschaftsinformatik 2015, Master Wirtschaftsinformatik 2018 ATTENTION: not offered anymore |
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|---|---|
| module name | Knowledge Discovery in Databases |
| examination number | 2200212 |
| credit points | 5 |
| on-campus program (h) | 34 |
| self-study (h) | 116 |
| obligation | elective module |
| exam | oral examination performance, 30 minutes |
| details of the certificate | |
| link to Moodle course | |
| signup details for alternative examinations | |
| maximum number of participants | |