| Knowledge Discovery in Databases [2021 SoSe] | ||
|---|---|---|
| Code IKDD |
Name Knowledge Discovery in Databases |
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| LP 8 LP |
Dauer one semester |
Angebotsturnus every 2nd winter semester |
| Format Lecture 4 SWS + Exercise course 2 SWS |
Arbeitsaufwand 240 h; thereof 90 h lecture 20 h preparation for exam 130 h self-study and working on assignments/projects (optionally in groups) |
Verwendbarkeit B.Sc. Angewandte Informatik, M.Sc. Angewandte Informatik, M.Sc. Scientific Computing |
| Sprache |
Lehrende |
Prüfungsschema |
| Lernziele | Students - understand the KDD process and when to apply different KDD tasks - are able to apply suitable data mining techniques to specific data analysis problem - know the foundations of statistics and probability theory underlying diverse data mining techniques - can apply and adopt different data clustering algorithms and models - can apply and adopt different data classification algorithms and models - understand different methods and metrics to evaluate the quality of data mining results - can describe different pattern detection methods to obtain frequent patterns from diverse types of data sets - are familiar with the foundations of models and techniques to extract patterns from graph data - can apply and realize the different algorithms and data mining procedures in software environments such as R or Python |
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| Lerninhalte | - KDD process and tasks - Data, statistics, and probability theory - Clustering models, techniques, and algorithms - Classification models, techniques, and algorithms - Frequent pattern mining approaches - Outlier detection concepts - Graph mining models and methods |
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| Teilnahme- voraus- setzungen |
recommended are: Algorithmen und Datenstrukturen (IAD), Effiziente Algorithmen 1 (IEA1), Einführung in die Wahrscheinlichkeitstheorie und Statistik (MA8) | |
| Vergabe der LP und Modulendnote | Bestehen der Modulprüfung | |
| Nützliche Literatur | - Jiawei Han, Micheline Kamber, and Jian Pei: Data Mining. Concepts and Techniques, Morgan Kaufmann Series in Data Management Systems, 2011 (3rd Edition). - Charu Aggarwal: The textbook. Springer, 2015. - Pang-Ning Tan, Michael Steinbach, and Vipin Kumar: Introduction to Data Mining. Addison Wesley, 2005. |
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