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2020 – today
- 2023
[c16]Jonas Jepsen, Arthur Zamfir, Brigitte Boden, Jacopo Zamboni, Erwin Moerland:
On the Development of a Collaborative Knowledge Platform for Engineering Sciences. KEOD 2023: 208-215- 2021
- [j5]Brigitte Boden, Jan Flink, Niklas Först
, Robert Mischke, Kathrin Schaffert, Alexander Weinert, Annika Wohlan, Andreas Schreiber:
RCE: An Integration Environment for Engineering and Science. SoftwareX 15: 100759 (2021) - 2020
- [c15]Brigitte Boden, Robert Mischke, Alexander Weinert, Andreas Schreiber:
Supporting the composition of domain-specific software via task-specific roles. Programming 2020: 17-18
2010 – 2019
- 2019
- [i1]Brigitte Boden, Jan Flink, Robert Mischke, Kathrin Schaffert, Alexander Weinert, Annika Wohlan, Andreas Schreiber:
RCE: An Integration Environment for Engineering and Science. CoRR abs/1908.03461 (2019) - 2017
- [j4]Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl:
MiMAG: mining coherent subgraphs in multi-layer graphs with edge labels. Knowl. Inf. Syst. 50(2): 417-446 (2017) - 2014
- [j3]Stephan Günnemann, Ines Färber, Brigitte Boden, Thomas Seidl:
GAMer: a synthesis of subspace clustering and dense subgraph mining. Knowl. Inf. Syst. 40(2): 243-278 (2014) - [c14]Sergej Fries, Brigitte Boden, Grzegorz Stepien, Thomas Seidl:
PHiDJ: Parallel similarity self-join for high-dimensional vector data with MapReduce. ICDE 2014: 796-807 - [c13]Brigitte Boden, Martin Ester, Thomas Seidl:
Density-Based Subspace Clustering in Heterogeneous Networks. ECML/PKDD (1) 2014: 149-164 - 2013
- [b1]Brigitte Boden:
Combined clustering of graph and attribute data. RWTH Aachen University, Apprimus-Verlag 2013, ISBN 978-3-86359-216-5 - [c12]Thomas Seidl, Sergej Fries, Brigitte Boden:
MR-DSJ: Distance-Based Self-Join for Large-Scale Vector Data Analysis with MapReduce. BTW 2013: 37-56 - [c11]Brigitte Boden, Roman Haag, Thomas Seidl:
Detecting and exploring clusters in attributed graphs: a plugin for the gephi platform. CIKM 2013: 2505-2508 - [c10]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 - [c9]Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl:
RMiCS: a robust approach for mining coherent subgraphs in edge-labeled multi-layer graphs. SSDBM 2013: 23:1-23:12 - 2012
- [j2]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) - [j1]Brigitte Boden:
Clustering von Graphen mit Knotenbeschriftungen. Inform. Spektrum 35(3): 220-222 (2012) - [c8]Brigitte Boden, Stephan Günnemann, Thomas Seidl:
Tracing clusters in evolving graphs with node attributes. CIKM 2012: 2331-2334 - [c7]Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl:
Mining coherent subgraphs in multi-layer graphs with edge labels. KDD 2012: 1258-1266 - [c6]Thomas Seidl, Brigitte Boden, Sergej Fries:
CC-MR - Finding Connected Components in Huge Graphs with MapReduce. ECML/PKDD (1) 2012: 458-473 - [c5]Stephan Günnemann, Brigitte Boden, Thomas Seidl:
Substructure Clustering: A Novel Mining Paradigm for Arbitrary Data Types. SSDBM 2012: 280-297 - 2011
- [c4]Stephan Günnemann, Brigitte Boden, Thomas Seidl:
Finding Density-Based Subspace Clusters in Graphs with Feature Vectors. LWA 2011: 20-27 - [c3]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 - 2010
- [c2]Stephan Günnemann, Ines Färber, Brigitte Boden, Thomas Seidl:
Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms. ICDM 2010: 845-850 - [c1]Brigitte Boden:
Synthese von Subspace Clustering und Subgraph Mining zur Analyse von Graphen mit Knotenbeschriftungen. Informatiktage 2010: 141-144
Coauthor Index
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