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The Data Mining Group at University of Vienna

dc.contributor.authorAltinigneli, Can
dc.contributor.authorBauer, Lena Greta Marie
dc.contributor.authorBehzadi, Sahar
dc.contributor.authorFritze, Robert
dc.contributor.authorHlaváčková-Schindler, Kateřina
dc.contributor.authorLeodolter, Maximilian
dc.contributor.authorMiklautz, Lukas
dc.contributor.authorPerdacher, Martin
dc.contributor.authorSadikaj, Ylli
dc.contributor.authorSchelling, Benjamin
dc.contributor.authorPlant, Claudia
dc.date.accessioned2021-05-04T09:36:35Z
dc.date.available2021-05-04T09:36:35Z
dc.date.issued2020
dc.description.abstractHow 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.de
dc.identifier.doi10.1007/s13222-020-00337-9
dc.identifier.pissn1610-1995
dc.identifier.urihttp://dx.doi.org/10.1007/s13222-020-00337-9
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/36391
dc.publisherSpringer
dc.relation.ispartofDatenbank-Spektrum: Vol. 20, No. 1
dc.relation.ispartofseriesDatenbank-Spektrum
dc.subjectCausality
dc.subjectClustering
dc.subjectData Mining
dc.subjectMachine Learning
dc.subjectMassive Data
dc.titleThe Data Mining Group at University of Viennade
dc.typeText/Journal Article
gi.citation.endPage79
gi.citation.startPage71

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