Data Mining (FSS2014)
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 of the FSS 2014 Exam
- Monday, 15.09.2014 2.00-2.30pm, Room: tbd
Time and Location
- Wednesday, 10.15 - 11.45, room A5 B224, start: 12.02.2014
- Thursday, 13.45 - 15.15, room A5 B224, start: 13.02.2014
- 50 % written exam
- 50 % project work
- Submission of project proposals: 31.03.2014, 23:59 CET
- Submission of final project reports: 15.05.2014, 23:59 CET
- Exam: 2.6.2014
Slides and Excercises
The lecture slides and exercises are published on this web page.
The solutions to the exercises will be provided in ILIAS.
- Slideset: Introduction and Course Outline
- Slideset: Introduction to RapidMiner
- Exercise: Data Vizualization (DataSet)
- Slideset: Cluster Analysis
- Exercise: Clustering (DataSet)
- Slideset: Classification - Part 1
- Exercise: Classification - Part 1 (Classification Process)
- Slideset: Classification - Part 2
- Exercise: Classification - Part 2 (DataSet)
- Slideset: Classification - Part 3
- Exercise: Classification - Part 3 (DataSet)
- Slideset: Association Analysis
- Exercise: Association Analysis (DataSet)
- Slideset: Introduction to Student Projects
- Slideset: Text Mining
- Exercise: Text Mining (DataSet)
Participation FSS 2014
- The course is open to students of the Master Business Informatics and Lehramt Informatik.
- The course is restricted to 40 participants.
- Registration is done via the ILIAS group.
- Registration will be opened Wednesday, February 5th, 8:00 am
- Allocation of places is done by FCFS (limit 40 students)
- If you have any questions, please contact Robert Meusel (robert(at)informatik.uni-mannheim.de)
|12.02.2014||Introduction to Data Mining||Introduction to RapidMiner|
|19.02.2014||Lecture Clustering||Exercise Clustering|
|26.02.2014||Lecture Classification 1||Exercise Classification|
|05.03.2014||Lecture Classification 2||Exercise Classification|
|12.03.2014||Lecture Classification 3||Exercise Classification|
|19.03.2014||Lecture Association Analysis||Exercise Association Analysis|
|26.03.2014||Intro Student Projects||Lecture Text Mining|
|02.04.2014||Exercise Text Mining||Feedback Student Projects|
|08.04.2014||Feedback on demand||Project Work|
|30.04.2014||Feedback on demand||public holiday|
|07.05.2014||Feedback on demand||Project Work|
|14.05.2014||Feedback on demand||Submission of project summaries|
|21.05.2014||Presentation of project results||Presentation of project results|
|28.05.2014||Presentation of project results||public holiday|
- 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.