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
- 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.
- 60 % written exam
- 40 % project work
Slides and Excercises
- Lecture: Introduction and Course Organization
- Exercise: Preprocessing and Visualization (Task | Data)
- Lecture: Cluster Analysis
- Exercise: Cluster Analysis (Task | Data)
- Lecture: Classification - Part 1
- Exercise: Classification - Part 1 (Task)
- Lecture: Classification - Part 2
- Exercise: Classification - Part 2 (Task | Data)
- Lecture: Classification - Part 3
- Exercise: Classification - Part 3 (Task | Data)
- Lecture: Text Mining
- Exercise: Text Mining (Task | Data)
- Lecture: Introduction to the Student Projects
- If you have any questions, please contact Oliver Lehmberg (oli(at)informatik.uni-mannheim.de)
- 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.
|15.02.2017||Introduction to Data Mining||Introduction to RapidMiner|
|22.02.2017||Lecture Clustering||Exercise Clustering|
|01.03.2017||Lecture Classification 1||Exercise Classification|
|08.03.2017||Lecture Classification 2||Exercise Classification|
|15.03.2017||Lecture Classification 3||Exercise Classification|
|22.03.2017||Lecture Text Mining||Exercise Text Mining|
|29.03.2017||Introduction to Student Projects|
and Group Formation (Attendance obliatory)
|Preparation of Project Outline|
|05.04.2017||Lecture Association Analysis||Excersise Association Analysis+|
Feedback about Project Outlines
|- Easter break -|
|26.04.2017||Project Work||Feedback on demand|
|03.05.2017||Project Work||Feedback on demand|
|10.05.2017||Project Work||Feedback on demand|
|17.05.2017||Project Work||Feedback on demand|
|24.05.2017||Submission of project results||- Holiday -|
|31.05.2017||Presentation of project results||Presentation of project results|
- Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Pearson.
- Vijay Kotu, Bala Deshpande: Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner. Morgan Kaufmann.
- Bing Liu: Web Data Mining, 2nd Edition, Springer.
Videos and Screen Casts
- Video recordings of the Data Mining I lectures and screen casts of the exercises are available here.
- Evaluation from FSS 2016
- Evaluation from FSS 2015
- Evaluation from FSS 2014
- Evaluation from HWS 2013
- Evaluation from FSS 2013