Data Mining (FSS 2019)

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.

Exam Review

The exam review for the exam of FSS2019 will take place on Friday, 27 September, 14:00, building B6, 26 room C1.01 (use the blue door and go to the first floor). There is no second exam for FSS2019. The next oportunity to retake the project and exam is in HWS2019.

  • Time and Location

    • Lecture: Wednesday, 10.15 – 11.45, Room A5, B144
    • Exercise 1: Thursday, 10.15 – 11.45, Room B6, A1.04 (RapidMiner)
    • Exercise 2: Thursday, 12.00 – 13.30, Room B6, A1.04 (Python)
    • Exercise 3: Thursday, 13.45 – 15.15, Room B6, A1.04 (Python)

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

  • Instructors

  • Final exam

    • 60 % written exam
    • 40 % project work
  • Registration

    • For attending the course, please register for the lecture in Portal 2. The course is limited to 80 participants. There will be no “first come – first serve”. Students in higher semesters will be preferred, equally ranked students will be drawn randomly.
    • We offer three alternative times (Thursdays 12.00, 13.45 and 15.30) for the exercise session. Choose one and attend the exercise at the corresponding time (you don't have to register for it).

Slides and Exercises

The lecture slide sets and exercises will be published here every week.

  1. Slideset: Introduction and Organization
  2. Slideset: Cluster Analysis
  3. Slideset: Classification – Part 1
  4. Slideset: Classification – Part 2
  5. Slideset: Classification – Part 3
  6. Slideset: Regression
  7. Slideset: Text Mining
  8. Slideset: Introduction to Student Projects
  9. Slideset: Association Analysis
  10. Slideset: Student Project Presentations 


  1. Exercise 01: Python (slides | Introduction to Python | Task) | RapidMiner (slides) | Datasets & Task
  2. Exercise 02: Python (Notebooks) | RapidMiner (slides) | Datasets & Task
  3. Exercise 03: Python (Notebooks) | RapidMiner (slides) | Datasets & Task
  4. Exercise 04: Python (Notebooks) | RapidMiner (slides) | Datasets & Task 
  5. Exercise 05: Python (Notebooks) | RapidMiner (slides) | Datasets & Task
  6. Exercise 06: Python (Notebooks) | RapidMiner (slides) | Datasets & Task
  7. Exercise 07: Python (Notebooks) | RapidMiner (slides) | Datasets & Task
  8. Exercise 08: Python (Notebooks) | RapidMiner (slides) | Datasets & Task


13.02.2019Introduction to Data MiningIntroduction to RapidMiner/Python
20.02.2019Lecture ClusteringExercise Clustering
27.02.2019Lecture Classification 1Exercise Classification 
06.03.2019Lecture Classification 2Exercise Classification 
13.03.2019Lecture Classification 3Exercise Classification 
20.03.2019Lecture RegressionExercise Regression
27.03.2019Lecture Text Mining Exercise Text Mining
03.04.2019Introduction to Student Projects 
and Group Formation (Attendance obliatory)
Preparation of Project Outlines
10.04.2019Lecture Association AnalysisExercise Association Analysis
 - Easter Break –  
01.05.2019- Holiday -Feedback on demand
08.05.2019Project WorkFeedback on demand
15.05.2019Project WorkFeedback on demand
22.05.2019Project WorkSubmission of project results
28./29.05.2018Presentation of project results- Holiday -