Data Mining
Exam Paper Review: On April 23 from 10:00 to 11:00 students may view their
exam papers in room B6, A2.07.
The lecture will provide 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
- Basic Techniques (e.g. Clustering, Classification, Association Rule Mining)
- Advanced Topics (e.g. Web Mining , Social Network- or Process Mining)
- Systems and Applications (e.g. Retail, Finance)
In the accompanying practical exercises as well as in the student projects, participants will gather expertise in applying state of the art data mining tools on realistic data sets.
Students will acquire basic knowledge of the techniques, opportunities and applications of data mining. Successful participants will be able to identify opportunities for applying data mining in an enterprise environment, select appropriate techniques and interpret the results.
Time and Location
- Lecture: Friday, 12:00-13:30, room: A 5, C 013
- Exercise: Thursday, 10:15-11:45, room A 5, C 015
Instructor
- Prof. Dr. Christian Bizer
Final exam
- 50 % written exam
- 50 % project work
Slides and Excercises
- The lecture slides and excercises are provided in ILIAS.
Participation restriction
- The course is restricted to 20 students.
Outline
Date | Topic Lecture | Topic Exercise |
9.9.2011 | Introduction to Data Mining | Introduction to RapidMiner |
16.9.2011 | Clustering | Exercise Clustering |
23.9.2011 | Classification 1 | Exercise Classification |
30.9.2011 | Classification 2 | Exercise Classification |
7.10.2011 | Validation | Exercise Validation |
14.10.2011 | Association Analysis | Exercise Association Analysis |
21.10.2011 | Sequential Patterns | Exercise Sequential Patterns |
28.10.2011 | Text Mining | Exercise Text Mining |
4.11.2011 | Introduction to student projects | Project work |
11.11.2011 | Discusion of project outlines | Project work |
18.11.2011 | Project coaching | Project work |
25.11.2011 | Project coaching | Project work |
2.12.2011 | Presentation of project results | Presentation of project results |
9.12.2011 | Examination: 8.12.2011, 10:15 |
Literature
- 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.
Software
