Profile description and modules:

Description: 

Data mining, also known as Knowledge Discovery in Databases (KDD) is an automated process to discover new and interesting information in large quantities of data. 

Modules: 

Artificial Intelligence Methods; Machine Learning Methods, Visual Data Analysis

The programme focuses primarily on the following:

Data mining/Big Data imparts the principles of data management and analysis and advanced methods of information preparation and visualisation and also the required basic principles of core computer science. In the seminars and internships that accompany the program of study, knowledge of these methods is promoted and applied practically. 
The area of data mining is represented by two internationally-known researchers (Prof. D. Keim, Prof. M. Berthold). Their many contacts to countries beyond Europe frequently provide students with the opportunity to spend a part of their studies in the US, for example. 

Study Structure

Sample curriculum

A possible sample curriculum focusing on data mining could be as follows (lectures and classification can change over time):

1st term

  • Data mining 1
  • Multimedia database systems
  • Digital signal processing
  • Introducation to economics (different department)

2nd term

  • Data mining 2
  • Information visualisation 1
  • Anorganic chemistry and analytical chemistry 1 (different department)
  • Busines intelligence: from reporting to analytics

3rd term

  • Algorithms for the analysis of large volumes of data
  • Drawing of graphs
  • Stochastics (different department)
  • Text mining
  • Master project: Machine learning - implementing a hierarchical self-organising map

4th term

  • Master's thesis in the field of data mining, machine learning, artificial intelligence, information visualisation, information retrieval, e.g. Visual Clustering of Finance Arrays.

Research groups involved

Prof. Michael Berthold:  Bioinformatics and Information Mining 
Prof. Ulrik Brandes:  Algorithmics
Prof. Daniel Keim:  Data Analysis and Visualization 
Prof. Marc Scholl:  Database & Information Systems (DBIS)

Area of application:

As the quantity and complexity of stored data from science and industry continues to increase, the need for intelligent machine-supported and also expert-supported analysis methods of this data also increases.
Due to the high demands of data mining it has become an interface for a variety of areas of research, such as machine learning and information visualisation, artificial intelligence and human computer interaction. Naturally, the basic principles from the classical areas of computer science are required, such as on databases, algorithms and software engineering.

Laboratories and Features

The Powerwall

A 5.20 m x 2.15 m powerwall for the visualisation of huge quantities of data

KNIME

KNIME, pronounced [naim], is a modular data exploration platform that enables data flows - so-called "pipelines" to be visually combined. These are subsequently executed allowing the data to be "pumped through". Subsequently the results is inspected in interactive views of data and models. 
KNIME was (and continues to be) developed at the Chair for Bioinformatics and Information Mining. Michael Berthold`s work group utilises this platform for teaching and research purposes. Almost all the data mining methods developed by the work group have been integrated into KNIME. 

KNIME

Contact and Mentor recommendation:

Prof. Michael Berthold, AG Bioinformatics & Information Mining 
Prof. Daniel Keim, AG Data Analysis and Visualisation