 | 2012 |
| 24 |  | Hardy Kremer,
Stephan Günnemann,
Arne Held,
Thomas Seidl:
Mining of Temporal Coherent Subspace Clusters in Multivariate Time Series Databases.
PAKDD (1) 2012: 444-455 |
| 23 |  | 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) |
| 2011 |
| 22 |  | 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 |
| 21 |  | Emmanuel Müller,
Ira Assent,
Stephan Günnemann,
Thomas Seidl:
Scalable density-based subspace clustering.
CIKM 2011: 1077-1086 |
| 20 |  | Stephan Günnemann,
Ines Färber,
Emmanuel Müller,
Ira Assent,
Thomas Seidl:
External evaluation measures for subspace clustering.
CIKM 2011: 1363-1372 |
| 19 |  | 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 |
| 18 |  | 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 |
| 17 |  | Stephan Günnemann,
Emmanuel Müller,
Sebastian Raubach,
Thomas Seidl:
Flexible Fault Tolerant Subspace Clustering for Data with Missing Values.
ICDM 2011: 231-240 |
| 16 |  | Stephan Günnemann,
Hardy Kremer,
Charlotte Laufkötter,
Thomas Seidl:
Tracing Evolving Clusters by Subspace and Value Similarity.
PAKDD (2) 2011: 444-456 |
| 15 |  | 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 |
| 14 |  | Ira Assent,
Hardy Kremer,
Stephan Günnemann,
Thomas Seidl:
Pattern detector: fast detection of suspicious stream patterns for immediate reaction.
EDBT 2010: 709-712 |
| 13 |  | Emmanuel Müller,
Stephan Günnemann,
Ines Färber,
Thomas Seidl:
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data.
ICDM 2010: 1220 |
| 12 |  | Stephan Günnemann,
Ines Färber,
Brigitte Boden,
Thomas Seidl:
Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms.
ICDM 2010: 845-850 |
| 11 |  | Stephan Günnemann,
Hardy Kremer,
Ines Färber,
Thomas Seidl:
MCExplorer: Interactive Exploration of Multiple (Subspace) Clustering Solutions.
ICDM Workshops 2010: 1387-1390 |
| 10 |  | 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 |
| 9 |  | Stephan Günnemann,
Thomas Seidl:
Subgraph Mining on Directed and Weighted Graphs.
PAKDD (2) 2010: 133-146 |
| 8 |  | Stephan Günnemann,
Hardy Kremer,
Thomas Seidl:
Subspace Clustering for Uncertain Data.
SDM 2010: 385-396 |
| 7 |  | Philipp Kranen,
Stephan Günnemann,
Sergej Fries,
Thomas Seidl:
MC-Tree: Improving Bayesian Anytime Classification.
SSDBM 2010: 252-269 |
| 6 |  | Stephan Günnemann,
Ines Färber,
Hardy Kremer,
Thomas Seidl:
CoDA: Interactive Cluster Based Concept Discovery.
PVLDB 3(2): 1633-1636 (2010) |
| 2009 |
| 5 |  | Ira Assent,
Stephan Günnemann,
Hardy Kremer,
Thomas Seidl:
High-Dimensional Indexing for Multimedia Features.
BTW 2009: 187-206 |
| 4 |  | Stephan Günnemann,
Emmanuel Müller,
Ines Färber,
Thomas Seidl:
Detection of orthogonal concepts in subspaces of high dimensional data.
CIKM 2009: 1317-1326 |
| 3 |  | 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 |
| 2 |  | Emmanuel Müller,
Ira Assent,
Ralph Krieger,
Stephan Günnemann,
Thomas Seidl:
DensEst: Density Estimation for Data Mining in High Dimensional Spaces.
SDM 2009: 173-184 |
| 1 |  | Emmanuel Müller,
Stephan Günnemann,
Ira Assent,
Thomas Seidl:
Evaluating Clustering in Subspace Projections of High Dimensional Data.
PVLDB 2(1): 1270-1281 (2009) |