Emmanuel Müller

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Thomas SeidlProfessor of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), MCML, GermanyVerified email at dbs.ifi.lmu.de
Ira AssentProfessor of Computer Science, Aarhus University, Denmark; IAS-8, Forschungszentrum JülichVerified email at cs.au.dk
Stephan GünnemannProfessor of Computer Science, Technical University of MunichVerified email at in.tum.de
Davide MottinAssociate Professor, Department of Computer Science, Aarhus UniversityVerified email at cs.au.dk
Anton TsitsulinResearch Scientist, Google ResearchVerified email at google.com
Panagiotis KarrasProfessor, University of CopenhagenVerified email at di.ku.dk
Ines FärberData Management and Data ExplorationVerified email at cs.rwth-aachen.de
Jilles VreekenCISPA Helmholtz Center for Information SecurityVerified email at cispa.de
Alex BronsteinDan Broida Academic Chair, Schmidt Chair of AI, CS@TechnionVerified email at technion.ac.il
Bryan PerozziGoogle ResearchVerified email at google.com
Leman AkogluAssociate Professor, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Bart GoethalsProfessor of Computer Science, University of AntwerpVerified email at uantwerpen.be
Marwan HassaniAssistant Professor of Computer Science at Eindhoven University of TechnologyVerified email at tue.nl
Arthur ZimekUniversity of Southern DenmarkVerified email at imada.sdu.dk
Christoph QuixChristoph Quix is a senior researcher in the Life Science Informatics group at the FraunhoferVerified email at dbis.rwth-aachen.de
Philipp KranenMicrosoft ResearchVerified email at microsoft.com
Arvind Kumar ShekarLead expert AI validation, Bosch GmbHVerified email at de.bosch.com
M. Emin AkşehirliBooking.comVerified email at booking.com
Peer KrögerChristian-Albrechts-University KielVerified email at informatik.uni-kiel.de
Erich SchubertProfessor of Data Mining, TU Dortmund UniversityVerified email at cs.tu-dortmund.de

Emmanuel Müller

Professor of Computer Science, Technical University of Dortmund

Verified email at cs.tu-dortmund.de - Homepage

Data Mining Machine Learning Data Exploration Databases


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Deep one-class classification L Ruff, R Vandermeulen, N Goernitz, L Deecke, SA Siddiqui, A Binder, ... International conference on machine learning, 4393-4402, 2018	2459	2018
Deep semi-supervised anomaly detection L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, KR Müller, ... arXiv preprint arXiv:1906.02694, 2019	705	2019
HiCS: High contrast subspaces for density-based outlier ranking F Keller, E Muller, K Bohm 2012 IEEE 28th international conference on data engineering, 1037-1048, 2012	491	2012
Evaluating clustering in subspace projections of high dimensional data E Müller, S Günnemann, I Assent, T Seidl Proceedings of the VLDB Endowment 2 (1), 1270-1281, 2009	364	2009
Verse: Versatile graph embeddings from similarity measures A Tsitsulin, D Mottin, P Karras, E Müller Proceedings of the 2018 world wide web conference, 539-548, 2018	326	2018
Focused clustering and outlier detection in large attributed graphs B Perozzi, L Akoglu, P Iglesias Sánchez, E Müller Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014	296	2014
Graph clustering with graph neural networks A Tsitsulin, J Palowitch, B Perozzi, E Müller Journal of Machine Learning Research 24 (127), 1-21, 2023	277	2023
Netlsd: hearing the shape of a graph A Tsitsulin, D Mottin, P Karras, A Bronstein, E Müller Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018	186	2018
Statistical selection of relevant subspace projections for outlier ranking E Müller, M Schiffer, T Seidl 2011 IEEE 27th international conference on data engineering, 434-445, 2011	177	2011
On using class-labels in evaluation of clusterings I Färber, S Günnemann, HP Kriegel, P Kröger, E Müller, E Schubert, ... MultiClust: 1st international workshop on discovering, summarizing and using …, 2010	171	2010
DUSC: Dimensionality unbiased subspace clustering I Assent, R Krieger, E Müller, T Seidl seventh IEEE international conference on data mining (ICDM 2007), 409-414, 2007	168	2007
Ranking outlier nodes in subspaces of attributed graphs E Müller, PI Sánchez, Y Mülle, K Böhm 2013 IEEE 29th international conference on data engineering workshops (ICDEW …, 2013	147	2013
INSCY: Indexing subspace clusters with in-process-removal of redundancy I Assent, R Krieger, E Müller, T Seidl 2008 Eighth IEEE International Conference on Data Mining, 719-724, 2008	132	2008
Outlier ranking via subspace analysis in multiple views of the data E Müller, I Assent, P Iglesias, Y Mülle, K Böhm 2012 IEEE 12th international conference on data mining, 529-538, 2012	104	2012
Relevant subspace clustering: Mining the most interesting non-redundant concepts in high dimensional data E Müller, I Assent, S Günnemann, R Krieger, T Seidl 2009 Ninth IEEE International Conference on Data Mining, 377-386, 2009	97	2009
OutRank: ranking outliers in high dimensional data E Muller, I Assent, U Steinhausen, T Seidl 2008 IEEE 24th international conference on data engineering workshop, 600-603, 2008	95	2008
Discovering multiple clustering solutions: Grouping objects in different views of the data E Muller, S Gunnemann, I Farber, T Seidl 2012 IEEE 28th international conference on data engineering, 1207-1210, 2012	91	2012
Statistical selection of congruent subspaces for mining attributed graphs PI Sánchez, E Müller, F Laforet, F Keller, K Böhm 2013 IEEE 13th international conference on data mining, 647-656, 2013	88	2013
CMI: An information-theoretic contrast measure for enhancing subspace cluster and outlier detection HV Nguyen, E Müller, J Vreeken, F Keller, K Böhm Proceedings of the 2013 SIAM International Conference on Data Mining, 198-206, 2013	87	2013
VISA: visual subspace clustering analysis I Assent, R Krieger, E Müller, T Seidl ACM SIGKDD Explorations Newsletter 9 (2), 5-12, 2007	86	2007

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