Data Mining (FSS 2018)

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
  • Regression
  • 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, 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 (RapidMiner)

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

Instructors

Final exam

  • 60 % written exam
  • 40 % project work

Slides and Exercises

  1. Slideset: Introduction and Course Organisation
  2. Exercise: Preprocessing and Visualization (Task | Data)
  3. Slideset: Cluster Analysis
  4. Exercise: Cluster Analysis (Task | Data)
  5. Slideset: Classification - Part 1
  6. Exercise: Classification (Task)
  7. Slideset: Classification - Part 2
  8. Exercise: Classification (Task | Data)
  9. Slideset: Classification - Part 3
  10. Exercise: Classification (Task | Data)
  11. Slideset: Regression
  12. Exercise: Regression (Task | Data)
  13. Slideset: Text Mining
  14. Exercise: Text Mining (Task | Data)
  15. Slideset: Introduction to Student Projects
  16. Slideset: Association Analysis
  17. The lecture slidesets and exercises will be published here every week.

Contact Person

  • If you have any questions, please contact Oliver Lehmberg (oli(at)informatik.uni-mannheim.de) 

 Registration

  • The course is open to students of the Master Business Informatics, Lehramt Informatik and Mannheim Master in Data Science (MMDS).
  • The course is restricted to 80 participants.
  • Registration will be opened Wednesday, February 7th 2018, 10:15 am.
  • Registration is done via ILIAS using this link (once the registration is open)
  • Allocation of places is done by FCFS (limit  80 students)
  • We offer three alternative times (Thursdays 12.00, 13.45 and 15.30) for the exercise session. Sign-In to one of the three groups within ILIAS after you have registered for the course. The groups are restricted to 30 students each.

Outline

WeekWednesdayThursday
14.02.2018Introduction to Data MiningIntroduction to RapidMiner/Python
21.02.2018Lecture ClusteringExercise Clustering
28.02.2018Lecture Classification 1Exercise Classification 
07.03.2018Lecture Classification 2Exercise Classification 
14.03.2018Lecture Classification 3Exercise Classification 
21.03.2018Lecture RegressionExercise Regression
 - Easter Break -
11.04.2018Lecture Text Mining Exercise Text Mining
18.04.2018Introduction to Student Projects 
and Group Formation (Attendance obliatory)
Preparation of Project Outlines
25.04.2018Lecture Association AnalysisExercise Association Analysis
02.05.2018Project WorkFeedback on demand
09.05.2018Project WorkFeedback on demand
16.05.2018Project WorkFeedback on demand
21.05.2018Project WorkSubmission of project results
24.05.2018-Presentation of project results


Literature 

  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.

Software

Videos and Screen Casts

  • Video recordings of the Data Mining I lectures and screen casts of the exercises are available here.

 Course Evaluations