Data Mining (HWS 2016)

The course provides an introduction to advanced data analysis techniques as a basis for analyzing business data and providing input for decision support systems. The course will cover the following topics:

  • Goals and Principles of Data Mining
  • Data Representation and Preprocessing
  • Clustering
  • Classification
  • Association Analysis
  • Text Mining
  • Systems and Applications (e.g. Retail, Finance, Web Analysis)

The course consists of a lecture together with accompanying practical exercises as well as student team projects.  In the exercises the participants will gather initial expertise in applying state of the art data mining tools on realistic data sets. The team projects take place in the last third of the term. Within the projects, students realize more sophisticated data mining projects of personal choice and report about the results of their projects in the form of a written report as well as an oral presentation.

Time and Location

  • Lecture: Wednesday, 12.00 - 13.30, Room B6 23-25, A1.01
  • Exercise 1: Thursday, 12.00 - 13.30, Room B6 23-25, A1.04
  • Exercise 2: Thursday, 13.45 - 15.15 Room B6 23-25, A1.04

Note: there are two parallel exercise groups, you are supposed to only attend one.

Exam Review

The exam review for the HWS2016 Data Mining course will take place on Tuesday, 17.01.2017 at 10:00 a.m., Room B6, C1.01.

The exam review for the second exam of the HWS2016 Data Mining course will take place on Tuesday, 07.03.2017 at 11:00 a.m., in B6 Room C1.01.


Week Contents
09/05/15 Course Organization and Introduction
09/12/15 Clustering
09/19/15 Classification Part 1
09/26/15 Classification Part 2
10/03/15 Classification Part 3
10/10/15 Text Mining
10/17/15 Introduction to Student Projects
10/24/15 Association Analysis
10/31/15 Project work
11/07/15 Project work
11/14/15 Project work
11/21/15 Project work
11/28/15 Project work
12/05/15 Presentation


Final exam

  • 50 % written exam
  • 50 % project work

Slides and Excercises



  • The course is open to students of the Master Business Informatics and Lehramt Informatik.
  • The course is restricted to 60 participants.
  • Registration will be opened Thursday, September 1st, 2016, 9:00 am.
  • Registration is done via ILIAS using this link (once the registration is open)
  • Allocation of places is done by FCFS (limit  60 students)
  • We offer two alternative times (Thursdays 12.00 and 13.45) for the exercise session. Sign-In to one of both groups within ILIAS after you have registered for the course. The groups are restricted to 30 students each.


  1. Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Pearson.
  2. Vijay Kotu, Bala Deshpande: Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner. Morgan Kaufmann.
  3. Bing Liu: Web Data Mining, 2nd Edition, Springer.


Videos and Screen Casts

  • Video recordings of the Data Mining I lectures and screen casts of the exercises are available here (course taught by Christian Bizer, contents are widely identical).