Publikationen

32 Ergebnisse

2013

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  S.1089-1092 (2013) (Demo)
[ICDM 2013]

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 S.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 S.369-378 (2012)
[ICDM 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)
[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 S.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 S.352-360 (2012)
[KDD 2012] [slides]
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]
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 S.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 S.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 S.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 S.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 S.565-580 (2011) ECML PKDD 2011 Best Paper Award in Data Mining
[ECML PKDD 2011] [Full Text PDF] [Supplementary Material]
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 S.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  S.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 S.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 S.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 S.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 S.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. S.385-396 (2010)
[SDM 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]
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 S.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) S.1633-1636 (2010) (Demo)
[VLDB 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, S.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 S.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. S.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 S.1317-1326 (2009) (full paper acceptance rate 14.5%)
[CIKM 2009] [DOI: 10.1145/1645953.1646120]
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 S.2-13 (2009)
[OpenSubspace Project] [OSDM 2009]