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Stephan Günnemann
2010 – today
- 2013
[c36]- [c35]Jennifer H. Nguyen, Bo Hu, Stephan Günnemann, Martin Ester: Finding contexts of social influence in online social networks. SNAKDD 2013: 1
- [c34]Geng Li, Stephan Günnemann, Mohammed J. Zaki: Stochastic subspace search for top-k multi-view clustering. MultiClust@KDD 2013: 3
- [c33]Stephan Günnemann, Brigitte Boden, Ines Färber, Thomas Seidl: Efficient Mining of Combined Subspace and Subgraph Clusters in Graphs with Feature Vectors. PAKDD (1) 2013: 261-275
- [c32]Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl: RMiCS: a robust approach for mining coherent subgraphs in edge-labeled multi-layer graphs. SSDBM 2013: 23
- [c31]Hardy Kremer, Stephan Günnemann, Simon Wollwage, Thomas Seidl: Nesting the earth mover's distance for effective cluster tracing. SSDBM 2013: 34
- 2012
- [j4]Stephan Günnemann, Hardy Kremer, Charlotte Laufkötter, Thomas Seidl: Tracing Evolving Subspace Clusters in Temporal Climate Data. Data Min. Knowl. Discov. 24(2): 387-410 (2012)
- [j3]Stephan Günnemann, Brigitte Boden, Thomas Seidl: Finding density-based subspace clusters in graphs with feature vectors. Data Min. Knowl. Discov. 25(2): 243-269 (2012)
- [c30]Brigitte Boden, Stephan Günnemann, Thomas Seidl: Tracing clusters in evolving graphs with node attributes. CIKM 2012: 2331-2334
- [c29]Emmanuel Müller, Stephan Günnemann, Ines Färber, Thomas Seidl: Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data. ICDE 2012: 1207-1210
- [c28]Stephan Günnemann, Phuong Dao, Mohsen Jamali, Martin Ester: Assessing the Significance of Data Mining Results on Graphs with Feature Vectors. ICDM 2012: 270-279
- [c27]Hardy Kremer, Stephan Günnemann, Arne Held, Thomas Seidl: Effective and Robust Mining of Temporal Subspace Clusters. ICDM 2012: 369-378
- [c26]Stephan Günnemann, Hardy Kremer, Richard Musiol, Roman Haag, Thomas Seidl: A Subspace Clustering Extension for the KNIME Data Mining Framework. ICDM Workshops 2012: 886-889
- [c25]Stephan Günnemann, Ines Färber, Thomas Seidl: Multi-view clustering using mixture models in subspace projections. KDD 2012: 132-140
- [c24]Stephan Günnemann, Ines Färber, Kittipat Virochsiri, Thomas Seidl: Subspace correlation clustering: finding locally correlated dimensions in subspace projections of the data. KDD 2012: 352-360
- [c23]Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl: Mining coherent subgraphs in multi-layer graphs with edge labels. KDD 2012: 1258-1266
- [c22]Hardy Kremer, Stephan Günnemann, Arne Held, Thomas Seidl: Mining of Temporal Coherent Subspace Clusters in Multivariate Time Series Databases. PAKDD (1) 2012: 444-455
- [c21]Stephan Günnemann, Brigitte Boden, Thomas Seidl: Substructure Clustering: A Novel Mining Paradigm for Arbitrary Data Types. SSDBM 2012: 280-297
- 2011
- [c20]Emmanuel Müller, Ira Assent, Stephan Günnemann, Patrick Gerwert, Matthias Hannen, Timm Jansen, Thomas Seidl: A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases. BTW 2011: 347-366
- [c19]Emmanuel Müller, Ira Assent, Stephan Günnemann, Thomas Seidl: Scalable density-based subspace clustering. CIKM 2011: 1077-1086
- [c18]Stephan Günnemann, Ines Färber, Emmanuel Müller, Ira Assent, Thomas Seidl: External evaluation measures for subspace clustering. CIKM 2011: 1363-1372
- [c17]Stephan Günnemann, Hardy Kremer, Dominik Lenhard, Thomas Seidl: Subspace clustering for indexing high dimensional data: a main memory index based on local reductions and individual multi-representations. EDBT 2011: 237-248
- [c16]Stephan Günnemann, Emmanuel Müller, Sebastian Raubach, Thomas Seidl: Flexible Fault Tolerant Subspace Clustering for Data with Missing Values. ICDM 2011: 231-240
- [c15]Stephan Günnemann, Hardy Kremer, Charlotte Laufkötter, Thomas Seidl: Tracing Evolving Clusters by Subspace and Value Similarity. PAKDD (2) 2011: 444-456
- [c14]Stephan Günnemann, Brigitte Boden, Thomas Seidl: DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors. ECML/PKDD (1) 2011: 565-580
- [c13]Hardy Kremer, Stephan Günnemann, Anca Maria Ivanescu, Ira Assent, Thomas Seidl: Efficient Processing of Multiple DTW Queries in Time Series Databases. SSDBM 2011: 150-167
- [e1]Emmanuel Müller, Stephan Günnemann, Ira Assent, Thomas Seidl (Eds.): Proceedings of the 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings, Athens, Greece, September 5, 2011, in conjunction with ECML/PKDD 2011. CEUR Workshop Proceedings 772, CEUR-WS.org 2011
- 2010
- [j2]Stephan Günnemann, Ines Färber, Hardy Kremer, Thomas Seidl: CoDA: Interactive Cluster Based Concept Discovery. PVLDB 3(2): 1633-1636 (2010)
- [c12]Ira Assent, Hardy Kremer, Stephan Günnemann, Thomas Seidl: Pattern detector: fast detection of suspicious stream patterns for immediate reaction. EDBT 2010: 709-712
- [c11]Hardy Kremer, Stephan Günnemann, Thomas Seidl: Detecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing Techniques. ICDM Workshops 2010: 96-97
- [c10]Stephan Günnemann, Ines Färber, Brigitte Boden, Thomas Seidl: Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms. ICDM 2010: 845-850
- [c9]Emmanuel Müller, Stephan Günnemann, Ines Färber, Thomas Seidl: Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data. ICDM 2010: 1220
- [c8]Stephan Günnemann, Hardy Kremer, Ines Färber, Thomas Seidl: MCExplorer: Interactive Exploration of Multiple (Subspace) Clustering Solutions. ICDM Workshops 2010: 1387-1390
- [c7]Stephan Günnemann, Thomas Seidl: Subgraph Mining on Directed and Weighted Graphs. PAKDD (2) 2010: 133-146
- [c6]Stephan Günnemann, Hardy Kremer, Thomas Seidl: Subspace Clustering for Uncertain Data. SDM 2010: 385-396
- [c5]Philipp Kranen, Stephan Günnemann, Sergej Fries, Thomas Seidl: MC-Tree: Improving Bayesian Anytime Classification. SSDBM 2010: 252-269
2000 – 2009
- 2009
- [j1]Emmanuel Müller, Stephan Günnemann, Ira Assent, Thomas Seidl: Evaluating Clustering in Subspace Projections of High Dimensional Data. PVLDB 2(1): 1270-1281 (2009)
- [c4]Ira Assent, Stephan Günnemann, Hardy Kremer, Thomas Seidl: High-Dimensional Indexing for Multimedia Features. BTW 2009: 187-206
- [c3]Stephan Günnemann, Emmanuel Müller, Ines Färber, Thomas Seidl: Detection of orthogonal concepts in subspaces of high dimensional data. CIKM 2009: 1317-1326
- [c2]Emmanuel Müller, Ira Assent, Stephan Günnemann, Ralph Krieger, Thomas Seidl: Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data. ICDM 2009: 377-386
- [c1]Emmanuel Müller, Ira Assent, Ralph Krieger, Stephan Günnemann, Thomas Seidl: DensEst: Density Estimation for Data Mining in High Dimensional Spaces. SDM 2009: 173-184
Coauthor Index
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last updated on 2013-10-02 11:20 CEST by the dblp team