Publications

51 results

2013

Günnemann S., Färber I., Raubach S., Seidl T.:
Spectral Subspace Clustering for Graphs with Feature Vectors
Proc. IEEE International Conference on Data Mining (ICDM), Dallas, TX, USA P.231-240 (2013)
[ICDM 2013] [supplementary material]
Kremer H., Günnemann S., Held A., Seidl T.:
An Evaluation Framework for Temporal Subspace Clustering Approaches
Proc. IEEE International Conference on Data Mining Workshops (ICDMW), Dallas, TX, USA  P.1089-1092 (2013) (Demo)
[ICDM 2013]
Müller E., Günnemann S., Färber I., Seidl T.:
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data
Tutorial at the 30th International Conference on Machine Learning (ICML 2013), Atlanta, USA (2013)
[ICML 2013] [Tutorial Website]
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 P.23 (2013)
[SSDBM 2013] [Supplementary Material]
Kremer H., Günnemann S., Wollwage S., Seidl T.:
Nesting the Earth Mover's Distance for Effective Cluster Tracing
Proc. of the 25th International Conference on Scientific and Statistical Database Management (SSDBM), Baltimore, Maryland, USA P.34:1-34:4 (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 P.261-275 (2013)
[Supplementary Material]

2012

Günnemann S., Dao P., Jamali M., Ester M.:
Assessing the Significance of Data Mining Results on Graphs with Feature Vectors
Proc. IEEE International Conference on Data Mining (ICDM 2012), Brussels, Belgium P.270-279 (2012)
[ICDM 2012]
Günnemann S., Kremer H., Laufkötter C., Seidl T.:
Tracing Evolving Subspace Clusters in Temporal Climate Data
Data Mining and Knowledge Discovery Journal (DMKD), Vol. 24, Nr. 2 P.387-410 (2012)
[Full Text PDF (Springer Open Access)]
Kremer H., Günnemann S., Held A., Seidl T.:
Effective and Robust Mining of Temporal Subspace Clusters
Proc. IEEE International Conference on Data Mining (ICDM), Brussels, Belgium P.369-378 (2012)
[ICDM 2012]
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  P.2331-2334 (2012) (poster presentation)
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 P.243-269 (2012)
[Supplementary Material]
Günnemann S., Färber I., Seidl T.:
Multi-View Clustering Using Mixture Models in Subspace Projections
Proc. of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD 2012), Beijing, China P.132-140 (2012)
[KDD 2012] [Supplementary material]
Günnemann S., Färber I., Virochsiri K., Seidl T.:
Subspace Correlation Clustering: Finding Locally Correlated Dimensions in Subspace Projections of the Data
Proc. of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD 2012), Beijing, China P.352-360 (2012)
[KDD 2012] [slides]
Boden B., Günnemann S., Hoffmann H., Seidl T.:
Mining Coherent Subgraphs in Multi-Layer Graphs with Edge Labels
Proc. of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD 2012), Beijing, China P.1258-1266 (2012)
[KDD 2012]
Günnemann S.:
Subspace Clustering for Complex Data
Dissertation, Fakultät für Mathematik, Informatik und Naturwissenschaften, RWTH Aachen University. (2012) Tag der mündlichen Prüfung: 15.03.2012
[RWTH Bibliothek] [URN]
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 P.280-297 (2012)
[SSDBM 2012]
Müller E., Günnemann S., Färber I., Seidl T.:
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data
Tutorial at the 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2012), Kuala Lumpur, Malaysia (2012)
[PAKDD 2012] [Tutorial Website]
Kremer H., Günnemann S., Held A., Seidl T.:
Mining of Temporal Coherent Subspace Clusters in Multivariate Time Series Databases
Proceedings 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Kuala Lumpur, Malaysia P.444-455 (2012)  
[PAKDD 2012] [full text PDF]
Günnemann S., Kremer H., Musiol R., Haag R., Seidl T.:
A Subspace Clustering Extension for the KNIME Data Mining Framework
Proc. IEEE International Conference on Data Mining Workshops (ICDMW), Brussels, Belgium P.886-889 (2012) (Demo)
[ICDM 2012] [Download Page]
Müller E., Günnemann S., Färber I., Seidl T.:
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data
Tutorial at IEEE 28th International Conference on Data Engineering (ICDE 2012), Washington, DC, USA (2012)
[ICDE 2012] [Tutorial Website]

2011

Günnemann S., Müller E., Raubach S., Seidl T.:
Flexible Fault Tolerant Subspace Clustering for Data with Missing Values
Proc. IEEE International Conference on Data Mining (ICDM 2011), Vancouver, Canada (2011)
[ICDM 2011]
Günnemann S., Färber I., Müller E., Assent I., Seidl T.:
External Evaluation Measures for Subspace Clustering
Proc. 20th ACM Conference on Information and Knowledge Management (CIKM 2011), Glasgow, UK P.1363-1372 (2011)
[CIKM 2011]
Müller E., Assent I., Günnemann S., Seidl T.:
Scalable Density-Based Subspace Clustering
Proc. 20th ACM Conference on Information and Knowledge Management (CIKM 2011), Glasgow, UK P.1077-1086 (2011)
[CIKM 2011]
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 P.565-580 (2011) ECML PKDD 2011 Best Paper Award in Data Mining
[ECML PKDD 2011] [Full Text PDF] [Supplementary Material]
Kremer H., Günnemann S., Zimmer (née Ivanescu) A., Assent I., Seidl T.:
Efficient Processing of Multiple DTW Queries in Time Series Databases
Proc. of the 23nd International Conference on Scientific and Statistical Database Management (SSDBM 2011), Portland, Oregon, USA P.150-167 (2011)
[SSDBM 2011]
Günnemann S., Kremer H., Laufkötter C., Seidl T.:
Tracing Evolving Clusters by Subspace and Value Similarity
Proc. 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2011), Shenzhen, China P.444-456 (2011)
[PAKDD 2011]
Müller E., Günnemann S., Färber I., Seidl T.:
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data
Tutorial at SIAM International Conference on Data Mining (SDM 2011), Mesa, Arizona, USA. (2011)
[SDM 2011] [Tutorial Website]
Günnemann S., Kremer H., Lenhard D., Seidl T.:
Subspace Clustering for Indexing High Dimensional Data: A Main Memory Index based on Local Reductions and Individual Multi-Representations
Proc. International Conference on Extending Database Technology (EDBT/ICDT 2011), Uppsala, Sweden  P.237-248 (2011)
[EDBT 2011]
Günnemann S., Kremer H., Seidl T.:
An Extension of the PMML Standard to Subspace Clustering Models
Workshop on Predictive Model Markup Language (PMML) in conj. with the 17th ACM Conference on Knowledge Discovery and Data Mining (SIGKDD 2011), San Diego, CA, USA P.48-53 (2011)
[PMML 2011] [KDD 2011] [Full Text PDF]
Müller E., Assent I., Günnemann S., Gerwert P., Hannen M., Jansen T., Seidl T.:
A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases
Proc. 14th GI Conference on Database Systems for Business, Technology, and the Web (BTW 2011), Kaiserslautern, Germany P.347-366 (2011)
[BTW 2011]

2010

Müller E., Günnemann S., Färber I., Seidl T.:
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data
Tutorial at IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia P.1220 (2010)
[ICDM 2010] [Tutorial Website]
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 P.845-850 (2010)
[ICDM 2010] [Supplementary material]
Günnemann S., Kremer H., Seidl T.:
Subspace Clustering for Uncertain Data
Proc.  SIAM International Conference on Data Mining (SDM 2010), Columbus, Ohio, USA. P.385-396 (2010)
[SDM 2010]
Kranen P., Günnemann S., Fries S., Seidl T.:
MC-Tree: Improving Bayesian Anytime Classification
Proc. of the 22nd International Conference on Scientific and Statistical Database Management (SSDBM 2010), Heidelberg, Germany, Springer LNCS P.252-269 (2010)
[SSDBM 2010] [DOI: 10.1007/978-3-642-13818-8_19]
Günnemann S., Seidl T.:
Subgraph Mining on Directed and Weighted Graphs
Proc. 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2010), 21-24 June, 2010 - Hyderabad, India. Springer Lecture Notes in Artificial Intelligence (LNAI) P.133-146 (2010)
[PAKDD 2010]
Färber I., Günnemann S., Kriegel H., Kröger P., Müller E., Schubert E., Seidl T., Zimek A.:
On Using Class-Labels in Evaluation of Clusterings
Proc. 1st International Workshop on Discovering, Summarizing and Using Multiple Clusterings (MultiClust 2010) in conjunction with 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), Washington, DC, USA (2010)
[MultiClust 2010] [Full Text PDF] [Talk Slides]
Günnemann S., Färber I., Müller E., Seidl T.:
ASCLU: Alternative Subspace Clustering
Proc. 1st International Workshop on Discovering, Summarizing and Using Multiple Clusterings (MultiClust 2010) in conjunction with 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), Washington, DC, USA (2010)
[MultiClust 2010] [Full Text PDF] [Talk Slides]
Assent I., Müller E., Günnemann S., Krieger R., Seidl T.:
Less is More: Non-Redundant Subspace Clustering
Proc. 1st International Workshop on Discovering, Summarizing and Using Multiple Clusterings (MultiClust 2010) in conjunction with 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), Washington, DC, USA (2010)
[MultiClust 2010] [Full Text PDF] [Talk Slides]
Kremer H., Günnemann S., Seidl T.:
Detecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing Techniques
Proc. 2nd IEEE ICDM Workshop on Knowledge Discovery from Climate Data: Prediction, Extremes, and Impacts (CLIMKD 2010) in conjunction with IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia P.96-97 (2010)
[ICDM 2010] [CLIMKD 2010]
Günnemann S., Kremer H., Färber I., Seidl T.:
MCExplorer: Interactive Exploration of Multiple (Subspace) Clustering Solutions
Proc. IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia P.1387-1390 (2010) (Demo)
[ICDM 2010]
Günnemann S., Färber I., Kremer H., Seidl T.:
CoDA: Interactive Cluster Based Concept Discovery
Proceedings of the VLDB Endowment (PVLDB) 3(2): 1633-1636 (2010) P.1633-1636 (2010) (Demo)
[VLDB 2010]
Assent I., Kremer H., Günnemann S., Seidl T.:
Pattern Detector: Fast Detection of Suspicious Stream Patterns for Immediate Reaction
Proc. International Conference on Extending Database Technology (EDBT/ICDT 2010), Lausanne, Switzerland. P.709-712 (2010) (Demo)
[EDBT/ICDT 2010]

2009

Müller E., Günnemann S., Assent I., Seidl T.:
Evaluating Clustering in Subspace Projections of High Dimensional Data
Proc. 35th International Conference on Very Large Data Bases (VLDB 2009), Lyon, France, PVLDB Journal, Vol. 2, No. 1, P.1270-1281 (2009) (Experiments and Analyses track, acceptance rate 23.1%)
[VLDB 2009] [Full Text PDF] [Supplementary material]
Müller E., Assent I., Günnemann S., Krieger R., Seidl T.:
Relevant Subspace Clustering: Mining the Most Interesting Non-Redundant Concepts in High Dimensional Data
Proc. IEEE International Conference on Data Mining (ICDM 2009), Miami, USA P.377-386 (2009) (full paper acceptance rate 8.9%)
[ICDM 2009] [Supplementary material]
Müller E., Assent I., Krieger R., Günnemann S., Seidl T.:
DensEst: Density Estimation for Data Mining in High Dimensional Spaces
Proc. SIAM International Conference on Data Mining (SDM 2009), Sparks, Nevada, USA. P.173-184 (2009) (full paper acceptance rate 15.6%)
[SDM 2009] [PDF]
Günnemann S., Müller E., Färber I., Seidl T.:
Detection of Orthogonal Concepts in Subspaces of High Dimensional Data
Proc. 18th ACM Conference on Information and Knowledge Management (CIKM 2009), Hong Kong, China P.1317-1326 (2009) (full paper acceptance rate 14.5%)
[CIKM 2009] [DOI: 10.1145/1645953.1646120]
Assent I., Günnemann S., Kremer H., Seidl T.:
High-dimensional Indexing for Multimedia Features
Proc. 13th GI Conference on Database Systems for Business, Technology, and the Web (BTW 2009), Münster, Germany. Lecture Notes in Informatics (LNI P-144). P.187-206 (2009)
[BTW 2009]
Müller E., Assent I., Günnemann S., Jansen T., Seidl T.:
OpenSubspace: An Open Source Framework for Evaluation and Exploration of Subspace Clustering Algorithms in WEKA
Proc. 1st Open Source in Data Mining Workshop (OSDM 2009) in conjunction with 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009), Bangkok, Thailand P.2-13 (2009)
[OpenSubspace Project] [OSDM 2009]