In dieser Projektgruppe werden grundlegende Algorithmen aus den Bereichen Wissensentdeckung und Data Mining erarbeitet und diskutiert. Im Mittelpunkt steht die Erarbeitung der Inhalte für verschiedene Zielgruppen. Die Aufgabe der Studierenden ist die Vermittlung von Wissen in Form eines Coding Nuggets als praktische Anleitung für Fachleute als auch in Form eines Blog Beitrags für interessierte Laien. Dabei wird auf die unterschiedlichen Anforderungen bei der Wissenvermittlung für unterschiedliche Zielgruppen eingegangen.

With more and more data available for analysis and decision making - from web documents and digital media to sensory data from cameras, microphones, and ubiquitous devices - it becomes increasingly more important to understand how such large volumes of data can be analyzed by computers and used as the basis for new intelligent services, for decision making, and for making computers learn from experience. In companies around the world, from retail and banks all the way to Google, intelligent learning and analysis techniques are used to improve business decisions. Likewise, in science, important discoveries are made easier by automated learning methods, and games and other artifacts are being made adaptive with learning technology.

Within the intelligent systems track of the computer science Master's program, intelligent learning and analysis systems are one of the two major topics. This module (Data Mining) is one of the two modules that are offered as an introduction for master's students to Intelligent Learning and Analysis Systems. The other is the Machine Learning module taught in the winter semester. Both modules can be selected in either order, and you may choose to attend one or both of them. For a complete introduction to the topic, it is recommended to attend both modules.

In the Data Mining module in particular, we will focus more on the algorithms for discovering knowledge in large databases and on their technical properties such as scalability. We will get to know scalable variants of the decision tree methods that we have been looking at in the Machine Learning module, and discover algorithms for new Data Mining tasks that we have not been looking at there, in particular clustering, association rule discovery, subgroup discovery, discovery from spatial and geographic data, analysis algorithm for text and web documents, visualization options for data analysis. We will mostly be focusing on practical and algorithmic aspects which can be tried out with popular Data Mining packages, but will also have a chance to look at some of the theory behind the algorithms.

The course offers an in-depth knowledge of different aspects of big data analytics and systems, including algorithmic techniques for analyzing structured and unstructured data that cannot be stored in a single computer because it has enormous size and/or continuously arrives with such a high rate that requires immediate processing. In addition to the algorithmic aspects, distributed big data processing and database systems will be presented and applied.

Topics include similarity search, synopses for massive data, mining massive graphs, classical data mining tasks for massive data and/or data streams, architectures and protocols for big data systems, distributed batch (Hadoop) and stream (Storm) processing systems, non-standard databases for big data (Cassandra).