Data Mining (FSS 2017)

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: Wednesday, 10.15 - 11.45, Room A5, B 144
  • Exercise 1: Thursday, 10.15 - 11.45, Room B6, A1.04
  • Exercise 2: Thursday, 12.00 - 13.30, Room B6, A1.04
  • Exercise 3: Thursday, 13.45 - 15.15 Room B6, A1.04

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 Excercises

  1. Lecture: Introduction and Course Organization
  2. Exercise: Preprocessing and Visualization (Task | Data)
  3. Lecture: Cluster Analysis
  4. Exercise: Cluster Analysis (Task | Data)
  5. Lecture: Classification - Part 1
  6. Exercise: Classification - Part 1 (Task)
  7. Lecture: Classification - Part 2
  8. Exercise: Classification - Part 2 (Task | Data)
  9. Lecture: Classification - Part 3
  10. Exercise: Classification - Part 3 (Task | Data)
  11. Lecture: Text Mining
  12. Exercise: Text Mining (Task | Data)
  13. Lecture: Introduction to the Student Projects

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 8th 2017, 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 10.00, 12.00 and 13.45) 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
15.02.2017Introduction to Data MiningIntroduction to RapidMiner
22.02.2017Lecture ClusteringExercise Clustering
01.03.2017Lecture Classification 1Exercise Classification 
08.03.2017Lecture Classification 2Exercise Classification 
15.03.2017Lecture Classification 3Exercise Classification 
22.03.2017Lecture Text MiningExercise Text Mining
29.03.2017Introduction to Student Projects
and Group Formation (Attendance obliatory)
Preparation of Project Outline
05.04.2017Lecture Association AnalysisExcersise Association Analysis+
Feedback about Project Outlines
- Easter break -
26.04.2017Project WorkFeedback on demand
03.05.2017Project WorkFeedback on demand
10.05.2017Project WorkFeedback on demand
17.05.2017Project WorkFeedback on demand
24.05.2017Submission of project results- Holiday -
31.05.2017Presentation of project resultsPresentation 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.
  3. Bing Liu: Web Data Mining, 2nd Edition, Springer.

Software

Videos and Screen Casts

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

 Course Evaluations