| Deep one-class classification L Ruff, R Vandermeulen, N Goernitz, L Deecke, SA Siddiqui, A Binder, ... International conference on machine learning, 4393-4402, 2018 | 2270 | 2018 |
| Deep semi-supervised anomaly detection L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, KR Müller, ... arXiv preprint arXiv:1906.02694, 2019 | 658 | 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 | 474 | 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 | 363 | 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 | 318 | 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 | 286 | 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 | 232 | 2023 |
| 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 | 175 | 2011 |
| 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 | 173 | 2018 |
| 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 | 169 | 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 | 144 | 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 | 103 | 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 | 94 | 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 | 90 | 2012 |
| 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 | 85 | 2007 |
| 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 | 84 | 2013 |
Thomas SeidlProfessor of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), MCML, Germany在 dbs.ifi.lmu.de 的电子邮件经过验证
