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 | 2013 | |
|---|---|---|
| c32 |      | |
| c31 | | 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 |
| 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 |
| 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 |
| 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 |
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