Data Mining (FSS 2018)

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
• Clustering
• Classification
• Regression
• 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.

The review for the first and second exam will take place on Friday, 28 September, at 13:00 in Room B6 C101.

• Lecture: Wednesday, 10.15 – 11.45, Room A5, B144
• Exercise 1: Thursday, 10.15 – 11.45, Room B6, A1.04 (RapidMiner)
• Exercise 2: Thursday, 12.00 – 13.30, Room B6, A1.04 (Python)
• Exercise 3: Thursday, 13.45 – 15.15, Room B6, A1.04 (RapidMiner)

Note: there are three parallel exercise groups, you are supposed to only attend one.

• Prof. Dr. Christian Bizer
• Oliver Lehmberg
• Kiril Gashteovski
• Daniel Ruffinelli

• 60 % written exam
• 40 % project work