Altinigneli, CanBauer, Lena Greta MarieBehzadi, SaharFritze, RobertHlaváčková-Schindler, KateřinaLeodolter, MaximilianMiklautz, LukasPerdacher, MartinSadikaj, YlliSchelling, BenjaminPlant, Claudia2021-05-042021-05-0420202020http://dx.doi.org/10.1007/s13222-020-00337-9https://dl.gi.de/handle/20.500.12116/36391How can we extract meaningful knowledge from massive amounts of data? The data mining group at University of Vienna contributes novel methods for exploratory data analysis. Our main research focus is on unsupervised learning, where we want to identify any kind of non-random structure or patterns in the data without restricting ourselves to a pre-defined target variable or analysis goal. Our major lines of current research are clustering, causality detection and highly efficient exploratory data analysis on massive data. Besides that, we develop application-specific methods addressing specific challenges in biomedicine, neuroscience and environmental sciences. In teaching, we offer fundamental and advanced courses in data mining, machine learning and scientific data management for Bachelor and Master students of computer science and related programs.CausalityClusteringData MiningMachine LearningMassive DataText/Journal Article10.1007/s13222-020-00337-91610-1995