Stephan Günnemann
Stephan Günnemann
Professor of Computer Science, Technical University of Munich
Verified email at in.tum.de - Homepage
TitleCited byYear
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
2522009
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
1072010
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
982010
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
802012
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
742009
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
652014
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
632012
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
512011
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
432009
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
422016
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
422014
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
422011
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
402009
Linearized and single-pass belief propagation
W Gatterbauer, S Günnemann, D Koutra, C Faloutsos
Proceedings of the VLDB Endowment 8 (5), 581-592, 2015
332015
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
332013
Subspace clustering for uncertain data
S Günnemann, H Kremer, T Seidl
Proceedings of the 2010 SIAM International Conference on Data Mining, 385-396, 2010
332010
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
A Bojchevski, S Günnemann
International Conference on Learning Representations (ICLR), 2018
31*2018
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
312014
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
312013
Tracing evolving subspace clusters in temporal climate data
S Günnemann, H Kremer, C Laufkötter, T Seidl
Data mining and knowledge discovery 24 (2), 387-410, 2012
29*2012
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Articles 1–20