Data Mining (HWS2014)

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: Tuesday, 12.00 - 13.30, room A5, B244, start: 02.09.2014
  • Exercise: Thursday, 10.15 - 11.45, room A5 B144 / 12.00 - 13.30, room B6 A104,  start: 04.09.2014  
  • Project Presentations: Thursdays (27.11.2014 and 04.12.2014), 10.15 - 13.30 room A5 B144 and Tuesday (02.12.2014), 12.00-13.00, room A5 B244 (presence of all participating students is mandatory)


Final exam

  • 50 % written exam
  • 50 % project work

 Important Deadlines

  • Submission of project proposals: October 26th 2014, 23:59
  • Submission of final project reports:  November 23rd 2014, 23:59
  • Exam: 

 Slides and Excercises

The lecture slides and exercises are published on this web page.
The solutions to the exercises will be provided in ILIAS.

 Participation HWS 2014

  • The course is open to students of the Master Business Informatics and Lehramt Informatik.
  • The course is restricted to 40 60 participants.
  • Registration is done via the ILIAS group
  • Registration will be opened Monday, August 25th, 10:00 am 
  • Allocation of places is done by FCFS (limit 40 60 students)
  • This semester we offer two alternative times (Thursdays 10.15 or Thursdays 12.00) for the exercise sessions. Sign-In to the one of both groups within ILIAS. The groups are restricted to 30 students per date.

 Course Evaluations

Exam Review of the FSS 2014 Exam

  • Monday, 15.09.2014 2.00-2.30pm, Room: B6, 26 A2.06

 Contact Person

  • If you have any questions, please contact Robert Meusel (robert(at) 

 Outline (preliminary)

Week TuesdayThursday
01.09.2014Introduction to Data Mining Introduction to RapidMiner
08.09.2014Lecture ClusteringExercise Clustering
15.09.2014Lecture Classification 1Exercise Classification 
22.09.2014Lecture Classification 2Exercise Classification 
29.09.2014Lecture Classification 3Exercise Classification 
06.10.2014Lecture Association AnalysisExercise Association Analysis
13.10.2014Lecture Text MiningExercise Text Mining
20.10.2014Intro Student ProjectsProject Work
27.10.2014Feedback Student ProjectsFeedback on demand
03.11.2014Project WorkFeedback on demand
10.11.2014Project WorkFeedback on demand
17.11.2014Project WorkFeedback on demand
24.11.2014Sunday Night: Submission of project summariesPresentation of project results
01.12.2014Presentation of project resultsPresentation of project results


  1. Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Pearson.
  2. Ian H. Witten, Eibe Frank, Mark A. Hall: Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, Morgan Kaufmann.
  3. Bing Liu: Web Data Mining, 2nd Edition, Springer.