Data Mining (HWS2013)

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

  • Tuesday, 15.30 - 17.00, room A101 in B6, start: 03.09.2013
  • Thursday, 10.15 - 11.45, room A101 in B6,  start: 05.09.2013


Final exam

  • 50 % written exam
  • 50 % project work

 Important Deadlines

  • Submission of project proposals: Monday, October 28th, 23:59
  • Submission of final project reports: Monday, November 25th, 23:59
  • Exam: Monday, December 16th

 Slides and Excercises

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

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 - 10am: 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. 

 Participation FSS 2014

  • Due to the high demand, this course also will be held in the FSS 2014
  • Registration is done via the ILIAS group (link will be posted as soon as possible)
  • Registration will be opened Wednesday, February 5th, 8:00 am 
  • Allocation of places is done by FCFS (limit 40 students)
  • Due to the high demand in the past semester we recommend to sign-in for the ILIAS group as soon as possible

 Course Evaluations

Exam Review of the FSS 2013 Exam

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

 Contact Person

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


Week Tuesday Thursday
02.09.2013 Introduction to Data Mining Introduction to RapidMiner
09.09.2013 Lecture Clustering Exercise Clustering
16.09.2013 Lecture Classification 1 Lecture Classification 2
23.09.2013 Exercise Classification Lecture Classification 3
30.09.2013 Exercise Classification (ctd.) public holiday
07.10.2013 Exercise Evaluation Lecture Association Analysis
14.10.2013 Exercise Association Analysis Lecture Text Mining
21.10.2013 Exercise Text Mining Intro Student Projects
28.10.2013 Project Work Feedback Student Projects
04.11.2013 Project Work Feedback on demand
11.11.2013 Project Work Feedback on demand
18.11.2013 Project Work Feedback on demand
25.11.2013 Submission of project summaries Presentation of project results
02.12.2013 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.