Data Mining (FSS2013)

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
  • Sequential Patterns
  • 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, 10.15 - 11.45, room B144 in A5, start: 12.02.2013
  • Exercise: Wednesday, 12.00 - 13.30, room C012 in A5,  start: 13.02.2013


  • Prof. Dr. Christian Bizer
  • Robert Meusel

Final exam

  • 50 % written exam
  • 50 % project work

Exam Review

  • Wednesday, 25.09.2013 3:00-3:30pm, Room: B6 26, A2.06

Slides and Excercises

  • The lecture slides and excercises are provided in ILIAS.

Course Evaluation

Participation FSS 2013

  • The course is open to students of the Master Business Informatics and Lehramt Informatik.
  • The course is restricted to 40 participants. Students which could not attend in HWS2012 will be prefered. Those students will get an e-mail notification on wednesday 09.01.2013 with a password to join the ILIAS group. 
    Registration for all students will be opened tuesday 05.02.2013 at 10 am.
  • 5th Feb 2013: Registration is now open for all students.
  • 6th Feb 2013: We have reached the capacity limit of 40 students - all students applying now for the course will be automatically put into the waiting list within the ILIAS system and may only participate if other students cancel their registration.

Participation HWS 2013

  • The course is open to students of the Master Business Informatics and Lehramt Informatik.
  • The course is restricted to 40 participants.
  • 5th August 2013: Students which could not attend the Data Mining course in HWS2012 due to the strong demand and where put to the list of preferred students for the Data Mining Course for FSS2013 and HWS2013 and did not already attend in FSS2013 will receive access credentials to enter the ILIAS eLearning group.
    Note: The waiting list within ILIAS of the Data Mining Group FSS2013 will not be taken into account for this procedure.
    Note: The list of preferred students for the Data Mining Course from HWS2012 will be deleted after HWS2013. We will not put any new students on this list.
  • 26th August 2013: The ILIAS eLearning Group will be opened for all students to fill the remaining slots. Slots will be filled up by first-come-first-serve. 
  • In case of any questions feel free to contact Robert Meusel (robert(at)

Contact Person

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


Week Topic Lecture Topic Exercise
12.2.2013 Introduction to Data Mining Introduction to RapidMiner
19.2.2013 Cluster Analysis Exercise Clustering
26.2.2013 Exercise Clustering Classification
5.3.2013 Exercise Classification Classification
12.3.2013 Exercise Classification Association Analysis
19.3.2013 Association Analysis Exercise Association Analysis
9.4.2013 Sequential Patterns Text Mining
16.4.2013 Exercise Text Mining Introduction to student projects
23.4.2013 Discussion of project outlines Project work
30.4.2013 Coaching Project work
7.5.2013 Coaching Project work
14.5.2013 Coaching Project work
20.5.2013 Submission of project summaries Presentation of project results
28.5.2013 Presentation of project results Presentation 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.