[IKDD] - [de] - [Knowledge Discovery in Databases]

Knowledge Discovery in Databases [2021 Sommer]
Knowledge Discovery in Databases
8 LP
one semester
every 2nd winter semester
Lecture 4 SWS + Exercise course 2 SWS
240 h; thereof
90 h lecture
20 h preparation for exam
130 h self-study and working on assignments/projects (optionally in groups)
B.Sc. Angewandte Informatik,
M.Sc. Angewandte Informatik,
M.Sc. Scientific Computing
Lernziel 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
Inhalt - 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
Voraussetzungen recommended are: Algorithmen und Datenstrukturen (IAD), Effiziente Algorithmen 1 (IEA1), Einführung in die Wahrscheinlichkeitstheorie und Statistik (MA8)
final written exam
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.