Enterprise and IT Security

Der fortgeschrittene Master-Studiengang Enterprise and IT Security (ENITS) öffnet Türen und hilft auf dem Weg zur Führungsposition im Bereich IT-Sicherheit.
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Data Mining

Prerequisite

Requires basic knowledge of data bases, statistics and experience with a modern programming Language
Teaching methods Lecture/Lab
Learning target / Competences

- Introduction to data mining: overview, CRISP, data pre-processing, concepts of supervised and unsupervised learning, visual analytics
- Association rules
- Linear regression: simple linear regression, introduction to multiple linear regression
- Classification: logistic regression, decision trees, SVM
- Ensemble methods: bagging, random forests, boosting
- Clustering: K-means, K-medoids, Hierarchical clustering
- Evaluation and validation: cross-validation, assessing the statistical significance of data mining results
- Ethics and privacy
- Selection of advanced topics such as neural networks, outlier detection, relation to big data analysis
- In the lab, students apply data mining methods and algorithms to problem sets and develop data mining applications,
using tools such as R and RapidMiner
Duration 1
Hours per week 4.0
Overview
Classes 60
Individual / Group work: 120
Workload 180
ECTS 6.0
Requirements for awarding credit points

written exam, 60 Min. and report (Data Mining, Lab Data Mining)
Credits and grades

written exam, 60 min. (K60, Data Mining) and report (BE, Lab Data Mining)
Responsible person

Prof. Dr. Stephan Trahasch
Recommended semester 1
Frequency Every 2nd sem.
Lectures

Data Mining

Type Lecture
Nr. M+I803
Hours per week 2.0
Content
  • Introduction to data mining: overview, CRISP, data pre-processing, concepts of supervised and unsupervised learning, visual analytics
  • Association rules
  • Linear regression: simple linear regression, introduction to multiple linear regression
  • Classification: logistic regression, decision trees, SVM
  • Ensemble methods: bagging, random forests, boosting
  • Clustering: K-means, K-medoids, Hierarchical clustering
  • Evaluation and validation: cross-validation, assessing the statistical significance of data mining results
  • Ethics and privacy
  • Selection of advanced topics such as neural networks, outlier detection, relation to big data analysis
  • In the lab, students apply data mining methods and algorithms to problem sets and develop data mining applications, using tools such as R and RapidMiner
Literature

Aggarwal, C. C. (2015). Data Mining: The Textbook. SpringerLink : Bücher. Cham: Springer International Publishing.

Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd ed.). Burlington: Elsevier Science.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2014). An introduction to statistical learning: With applications in R (Corrected at 4th print). Springer texts in statistics. New York: Springer.

Witten, I. H., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques (3rd ed.). Burlington, MA: Morgan Kaufmann.

Data Mining Lab

Type Lab
Nr. M+I804
Hours per week 2.0
Content

See M+I803 Data Mining
Literature

Siehe M+I803 Data Mining
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