b-it Research School
Data Mining, Pattern Recognition and Machine Learning
Data mining, pattern recognition and learning are subfields of computer science concerned with algorithms and systems for the computer assisted analysis of large data sets, with the goal of uncovering hidden patterns or knowledge useful for classification, prediction and decision making, and with algorithms capable of processing input data with the goal of making a system adaptive to its environment and/or task.
For classical business data, the wide-spread use of electronic commerce and the arrival of new technologies like RFID tagging are causing an exploding amount of data, but also newer data types like text, speech, images and other multimedia material are creating large amounts of data for pattern recognition and data mining. From environmental surveillance to security applications, sensor networks and video tracking are becoming more and more important, and we are seeing the first autonomous devices being deployed that need data analysis and learning techniques to adapt to their environments.
- Sven Behnke, Autonomous Intelligent Systems
- Hermann Ney, Speech Analysis and Pattern Recognition
- Gerhard Lakemeyer, Knowledge-Based Systems and Cognitive Robotics
- Thomas Seidl, Data Management and Exploration
- Stefan Wrobel, Data Mining
Bold letters indicate the professors currently in charge of this research area.