13 Ergebnisse


Fries S., Boden B., Stepien G., Seidl T.:
PHiDJ: Parallel Similarity Self-Join for High-Dimensional Vector Data with MapReduce
Proc. IEEE 30th International Conference on Data Engineering (ICDE 2014), Chicago, IL, USA (2014)
[ICDE 2014]


Boden B., Haag R., Seidl T.:
Detecting and Exploring Clusters in Attributed Graphs
Proceedings of the 22nd ACM Conference on Information and Knowledge Management (CIKM 2013), San Francisco, CA, USA S.2505-2508 (2013) (Demo)
[CIKM 2013]
Boden B., Günnemann S., Hoffmann H., Seidl T.:
RMiCS: A Robust Approach for Mining Coherent Subgraphs in Edge-Labeled Multi-Layer Graphs
Proc. of the 25th International Conference on Scientific and Statistical Database Management (SSDBM 2013), Baltimore, Maryland, USA S.23 (2013)
[SSDBM 2013]
Günnemann S., Boden B., Färber I., Seidl T.:
Efficient Mining of Combined Subspace and Subgraph Clusters in Graphs with Feature Vectors
Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013), Gold Coast, Queensland, Australia S.261-275 (2013)
Seidl T., Fries S., Boden B.:
MR-DSJ: Distance-Based Self-Join for Large-Scale Vector Data Analysis with MapReduce
15. GI-Fachtagung Datenbanksysteme für Business, Technologie und Web (BTW 2013), Magdeburg, Germany S.37-56 (2013)


Boden B., Günnemann S., Seidl T.:
Tracing Clusters in Evolving Graphs with Node Attributes
Proceedings of The 21st ACM Conference on Information and Knowledge Management (CIKM 2012), Maui, USA  S.2331-2334 (2012) (poster presentation)
Boden B.:
Efficient Combined Clustering of Graph and Attribute Data
PhD Workshop of the 38th International Conference on Very Large Data Bases (VLDB 2012), Istanbul (2012)
[VLDB 2012]
Günnemann S., Boden B., Seidl T.:
Finding Density-Based Subspace Clusters in Graphs with Feature Vectors
Data Mining and Knowledge Discovery Journal (DMKD), Vol. 25, Nr. 2 S.243-269 (2012)
Günnemann S., Boden B., Seidl T.:
Substructure Clustering: A Novel Mining Paradigm for Arbitrary Data Types
Proc. of the 24th International Conference on Scientific and Statistical Database Management (SSDBM 2012), Chania, Greece S.280-297 (2012)
[SSDBM 2012]


Günnemann S., Boden B., Seidl T.:
DB-CSC: A density-based approach for subspace clustering in graphs with feature vectors
Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2011), Athens, Greece S.565-580 (2011) ECML PKDD 2011 Best Paper Award in Data Mining
[ECML PKDD 2011] [Full Text PDF]


Günnemann S., Färber I., Boden B., Seidl T.:
Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms
Proc. IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia S.845-850 (2010)
[ICDM 2010] [Supplementary material]