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.

 

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

Slides and Exercises

  • 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

  • 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).

Outline

WeekWednesdayThursday
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
29.05.2018-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