| 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 | 280 | 2009 |
| 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 | 114 | 2010 |
| Subspace clustering meets dense subgraph mining: A synthesis of two paradigms S Gunnemann, I Farber, B Boden, T Seidl 2010 IEEE International Conference on Data Mining, 845-850, 2010 | 113 | 2010 |
| Mining coherent subgraphs in multi-layer graphs with edge labels B Boden, S Günnemann, H Hoffmann, T Seidl Proceedings of the 18th ACM SIGKDD international conference on Knowledge …, 2012 | 95 | 2012 |
| Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2018 | 89* | 2018 |
| 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 | 79 | 2009 |
| Com2: fast automatic discovery of temporal (‘comet’) communities M Araujo, S Papadimitriou, S Günnemann, C Faloutsos, P Basu, A Swami, ... Pacific-Asia Conference on Knowledge Discovery and Data Mining, 271-283, 2014 | 78 | 2014 |
| Adversarial attacks on neural networks for graph data D Zügner, A Akbarnejad, S Günnemann Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 76* | 2018 |
| 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 | 66 | 2012 |
| Netgan: Generating graphs via random walks A Bojchevski, O Shchur, D Zügner, S Günnemann ICML 2018, 2018 | 63 | 2018 |
| Birdnest: Bayesian inference for ratings-fraud detection B Hooi, N Shah, A Beutel, S Günnemann, L Akoglu, M Kumar, D Makhija, ... Proceedings of the 2016 SIAM International Conference on Data Mining, 495-503, 2016 | 60 | 2016 |
| DB-CSC: a density-based approach for subspace clustering in graphs with feature vectors S Günnemann, B Boden, T Seidl Joint European Conference on Machine Learning and Knowledge Discovery in …, 2011 | 58 | 2011 |
| External evaluation measures for subspace clustering S Günnemann, I Färber, E Müller, I Assent, T Seidl Proceedings of the 20th ACM international conference on Information and …, 2011 | 47 | 2011 |
| Detecting anomalies in dynamic rating data: A robust probabilistic model for rating evolution S Günnemann, N Günnemann, C Faloutsos Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 46 | 2014 |
| DensEst: Density estimation for data mining in high dimensional spaces E Müller, I Assent, R Krieger, S Günnemann, T Seidl Proceedings of the 2009 SIAM International Conference on Data Mining, 175-186, 2009 | 45 | 2009 |
| Spectral subspace clustering for graphs with feature vectors S Günnemann, I Färber, S Raubach, T Seidl 2013 IEEE 13th International Conference on Data Mining, 231-240, 2013 | 42 | 2013 |
| Detection of orthogonal concepts in subspaces of high dimensional data S Günnemann, E Müller, I Färber, T Seidl Proceedings of the 18th ACM conference on Information and knowledge …, 2009 | 40 | 2009 |
| Linearized and single-pass belief propagation W Gatterbauer, S Günnemann, D Koutra, C Faloutsos Proceedings of the VLDB Endowment 8 (5), 581-592, 2015 | 37 | 2015 |
| Efficient mining of combined subspace and subgraph clusters in graphs with feature vectors S Günnemann, B Boden, I Färber, T Seidl Pacific-Asia Conference on Knowledge Discovery and Data Mining, 261-275, 2013 | 37 | 2013 |
| Robust multivariate autoregression for anomaly detection in dynamic product ratings N Günnemann, S Günnemann, C Faloutsos Proceedings of the 23rd international conference on World wide web, 361-372, 2014 | 35 | 2014 |
Thomas SeidlProfessor of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), GermanyVerified email at dbs.ifi.lmu.de
