Data Mining (HWS 2015)
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.
Exam Review (Klausureinsicht)
- The exam review for the FSS 2015 term will take place on 22.9.2015 at 16:00 (updated time) in building B6 room A2.07.
- The exam review for the HWS 2015 term will take place on 05.02.2016 at 14:00 in building B6 room C1.01.
Time and Location
- Lecture: Monday, 10.15 - 11.45, Room B6 23-25, A101
- Exercise 1: Thursday, 12.00 - 13.30, Room B6 23-25, A104
- Exercise 2: Thursday, 13.45 - 15.15, Room B6 23-25, A104
Note: there are two parallel exercise groups, you are supposed to only attend one.
- 50 % written exam
- 50 % project work
Slides and Excercises
The lecture slides and exercises will be published on this web page. Exercise solutions and additional material will be published via ILIAS.
- Introduction (slides)
- Exercise 01 - Introduction to RapidMiner (slides | task | dataset)
- Clustering (slides)
- Exercise 02 - Clustering (task | dataset)
- Classification Part 1 (slides)
- Exercise 03 - Classification (slides classification | slides evaluation | task)
- Classification - Part 2 (slides)
- Exercise 04 - Classification Part 2 (task | dataset)
- Classification - Part 3 (slides)
- Exercise 05 - Classification Part 3 (task | dataset)
- Text Mining (slides)
- Exercise 06 -Text Mining (task | dataset)
- Association Analysis (slides)
- Exercise 07 - Association Analysis (task | dataset)
- Introduction to Student Projects (slides)
- 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 and Lehramt Informatik.
- The course is restricted to 60 participants.
- Registration is done via ILIAS (link will follow).
- Registration will be opened Monday, August 24th 2015, 10:00 am using this link
- Allocation of places is done by FCFS (limit 60 students)
- We offer two alternative times (Thursdays 12.00 and 13.45) for the exercise session. Sign-In to one of both groups within ILIAS after you have registered for the course. The groups are restricted to 30 students each.
- Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Pearson.
- Ian H. Witten, Eibe Frank, Mark A. Hall: Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, Morgan Kaufmann.
- Bing Liu: Web Data Mining, 2nd Edition, Springer.
- RapidMiner (Version 6.5)
Videos and Screen Casts
- Video recordings of the Data Mining I lectures and screen casts of the exercises are available here.