Data Mining (HWS 2012)
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
- Sequential Patterns
- 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: Monday 11th March 2013, room B6, 26 A 2.06, from 4.00 pm to 4.30 pm.
Time and Location
- Lecture: Wednesday, 15:30 - 17:00, room C014 in A5, start: 5.9.2012
- Exercise: Thursday, 10.15 - 11.45, room C015 in A5, start: 6.9.2012
- Prof. Dr. Christian Bizer
- 50 % written exam
- 50 % project work
Slides and Excercises
- The lecture slides and excercises are provided in ILIAS.
- The course is open to students of the Master Business Informatics and Lehramt Informatik.
- There is no restriction on the number of participants.
|Week||Topic Lecture||Topic Exercise|
|5.9.2012||Introduction to Data Mining||Introduction to RapidMiner|
|12.9.2012||Cluster Analysis||Exercise Clustering|
|19.9.2012||Cluster Analysis||Exercise Clustering|
|10.10.2012||Association Analysis||Exercise Association Analysis|
|17.10.2012||Sequential Patterns||Text Mining|
|24.10.2012||Exercise Text Mining||Introduction to student projects|
|31.10.2012||Presentation of project outlines||Project work|
|7.11.2012||Project work||Project work|
|14.11.2012||Project work||Project work|
|21.11.2012||Project work||Project work|
|28.11.2012||Presentation of project results||Presentation of project results|
|5.12.2012||Presentation of project results||Preparation for the exam|
- Pang-Ning Tan, Michael Steinback, 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.