Many applications, e.g., in biomedicine, the web and sensor networks generate tremendous amounts of data.
However, having more data not automatically means gaining more knowledge. To exploit the opportunities in Big Data, we need intelligent and efficient algorithms translating the information in data into understandable knowledge.
The research group Data Mining headed by Prof. Dr. Claudia Plant investigates methods comprehensively supporting the process of knowledge discovery from Big Data.
In current research we mainly focus on information-theoretic methods.
In order to make the information in data measurable, we link data mining to data compression. If data contains non-random structure like dependencies or other patterns we can find them with a data mining algorithm. We use the gained knowledge about the found patterns to compress our data. The compression rate is a very general quality measure for data mining. Based on this idea we focus on three central aspects.
