Big Data Analytics
Our research addresses theoretic challenges in correlation analysis, (un-)supervised feature selection, cluster and outlier detection as well as practical challenges in efficient computation of these models in large and complex data. The development of novel techniques for heterogeneous data spaces is a particular challenge in this area. We overcome the information loss of traditional techniques on homogeneous data sources and utilize the huge potential that is still unused in heterogeneous databases. Our group investigates algorithms for the selection of relevant attributes in high dimensional data, correlations in multivariate data streams, and homophile structures in attributed graphs.