Statistic Methods in Data Mining

Prof. Dr. Gholamreza Nakhaeizadeh

Teaching assistant

Time and place

Monday 8-10 in He220

Exercise session
Monday 10-11 in He220


2 hours lecture + 1 hour exercise


  • Introduction to Data Mining
  • Data Mining Process:
    • Data Understanding
    • Data Pre-processing
    • Modelling
    • Model validation
  • Data Mining Algorithms:
    • Regression Analysis
    • Bayesian Classifiers
    • Discriminant Analysis
    • Cluster Analysis
    • Decision and Regression Trees
    • Artificial Neural Networks
    • Association Rules

Final exam

The final exam hasn't been scheduled yet.


Introduction (pdf)

Process, part 1 (pdf)

Process, part 2 (pdf)

Process, part 3 (pdf)

Decision Trees (pdf)

Association Rules (pdf)

Artificial Neural Networks (pdf)

Regression Analysis, part 1 (pdf)

Naïve Bayes (pdf)

Exercise sheets

Exercises, part 1 to 8 (pdf)


  • Hand, D.J., Mannila, H., Smyth, P.
    Principles of Data Mining
    MIT Press, 2001
  • Tan, P., Steinbach, M., Kumar, V.
    Introduction to Data Mining
    Addison Wesley, 2005
  • Han, J., Kamber, M.
    Data Mining, Concepts and Techniques
    Morgen Kaufmann, 2006