Publications

62 results

2013

Kremer H.:
Mining and Similarity Search in Temporal Databases
Dissertation, Fakultät für Mathematik, Informatik und Naturwissenschaften, RWTH Aachen University. (2013) Tag der mündlichen Prüfung: 14.10.2013
[PDF] [RWTH Bibliothek] [Apprimus-Verlag]
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]
Hassani M., Kim Y., Seungjin Choi, Seidl T.:
Effective Evaluation Measures for Subspace Clustering of Data Streams
The third Quality issues, measures of interestingness and evaluation of data mining models workshop (QIMIE'13), held in conjunction with PAKDD'13 conference (17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Gold Coast, Australia) P.342-353 (2013)
[QIMIE13] [PAKDD13]

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]
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)
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]
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 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]
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., Schiffer M., Seidl T.:
Statistical Selection of Relevant Subspace Projections for Outlier Ranking
Proc. IEEE 27th International Conference on Data Engineering (ICDE 2011), Hannover, Germany P.434-445 (2011) (full paper acceptance rate 19.8%)
[ICDE 2011]
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.:
Efficient Knowledge Discovery in Subspaces of High Dimensional Databases
Dissertation, Fakultät für Mathematik, Informatik und Naturwissenschaften, RWTH Aachen University. (2010) Tag der mündlichen Prüfung: 09.06.2010
[RWTH Bibliothek]
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]
Müller E., Schiffer M., Seidl T.:
Adaptive Outlierness for Subspace Outlier Ranking
Proc. 19th ACM Conference on Information and Knowledge Management (CIKM 2010), Toronto, Canada P.1629-1632 (2010)
[CIKM 2010]
Hassani M., Müller E., Spaus P., Faqolli A., Palpanas T., Seidl T.:
Self-Organizing Energy Aware Clustering of Nodes in Sensor Networks Using Relevant Attributes
Proc. 4th International Workshop on Knowledge Discovery from Sensor Data (SensorKDD 2010) in conjunction with 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), Washington, DC, USA P.87-96 (2010)
[SensorKDD 2010]
Müller E.:
Mining Subspace Clusters: Enhanced Models, Efficient Algorithms and an Objective Evaluation Study
PhD Workshop of the 36th International Conference on Very Large Data Bases (VLDB 2010), Singapore (2010)
[VLDB 2010] [Full Text PDF]
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 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]
Müller E., Schiffer M., Gerwert P., Hannen M., Jansen T., Seidl T.:
SOREX: Subspace Outlier Ranking Exploration Toolkit
Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010), Barcelona, Spain, Springer, LNAI 6323 P.607-610 (2010) (Demo)
[ECML PKDD 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]
Müller E., Assent I., Seidl T.:
HSM: Heterogeneous Subspace Mining in High Dimensional Data
Proc. 21st International Conference on Scientific and Statistical Database Management (SSDBM 2009), New Orleans, Louisiana, USA P.497-516 (2009)
[SSDBM 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]
Schiffer M., Müller E., Seidl T.:
SubRank: Ranking Local Outliers in Projections of High-Dimensional Spaces
Datenbank-Spektrum Vol. 9 Issue 29 P.53-55 (2009) (BTW-Studierendenprogramm)
[DB Spektrum]
Schiffer M.:
SubRank: Ranking local outliers in projections of high-dimensional spaces
Studierendenprogramm at the 13th GI-conference on Databases, Technology and Web (BTW 2009), Münster, Germany [pdf] (2009)
[BTW 2009 Studierendenprogramm]
Färber I.:
Mining orthogonaler Konzepte in hochdimensionalen Datenbanken
GI Informatiktage 27./28. März 2009 in Bonn P.153-156 (2009)
[Informatiktage]

2008

Krieger R.:
Efficient density-based methods for knowledge discovery in databases
Dissertation, Fakultät für Mathematik, Informatik und Naturwissenschaften, RWTH Aachen University. (2008) Tag der mündlichen Prüfung: 09.07.2008
[RWTH Bibliothek]
Assent I., Krieger R., Müller E., Seidl T.:
INSCY: Indexing Subspace Clusters with In-Process-Removal of Redundancy
Proc. IEEE International Conference on Data Mining (ICDM 2008), Pisa, Italy P.719-724 (2008) (acceptance rate 20%)
[ICDM 2008]
Assent I., Krieger R., Müller E., Seidl T.:
EDSC: Efficient Density-Based Subspace Clustering
Proc. ACM 17th Conference on Information and Knowledge Management (CIKM 2008), Napa Valley, USA P.1093-1102 (2008) (full paper acceptance rate 17%)
[CIKM 2008]
Assent I., Krieger R., Welter P., Herbers J., Seidl T.:
SubClass: Classification of Multidimensional Noisy Data Using Subspace Clusters
Proc. 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2008), Springer LNCS/LNAI, Osaka, Japan P.40-52 (2008)
[PAKDD 2008] [DOI: 10.1007/978-3-540-68125-0_6]
Müller E., Assent I., Krieger R., Jansen T., Seidl T.:
Morpheus: Interactive Exploration of Subspace Clustering
Proc. 14th ACM SIGKDD International Conference on Knowledge Discovery in Databases (KDD 2008), Las Vegas, USA P.1089-1092 (2008) (Demo)
[KDD 2008]
Müller E., Assent I., Steinhausen U., Seidl T.:
OutRank: ranking outliers in high dimensional data
Proc. 2nd International Workshop on Ranking in Databases (DBRank 2008) in conjunction with IEEE 24th International Conference on Data Engineering (ICDE 2008), Cancun, Mexico P.600-603 (2008)
[ICDE 2008 Workshops] [DOI: 10.1109/ICDEW.2008.4498387]
Assent I., Müller E., Krieger R., Jansen T., Seidl T.:
Pleiades: Subspace Clustering and Evaluation
Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2008), Antwerp, Belgium, Springer LNCS 5212. P.666-671 (2008) (Demo)
[ECML PKKD 2008] [Springer LNCS 5212]
Seidl T., Müller E., Assent I., Steinhausen U.:
Outlier detection and ranking based on subspace clustering
Dagstuhl Seminar 08421 on Uncertainty Management in Information Systems. (2008)
[Dagstuhl seminar 08421] [paper at DROPS]

2007

Assent I., Krieger R., Müller E., Seidl T.:
VISA: Visual Subspace Clustering Analysis
ACM SIGKDD Explorations Special Issue on Visual Analytics, Vol. 9, Issue 2 P.5-12 (2007)
[SIGKDD Explorations] [Full Text PDF]
Assent I., Krieger R., Müller E., Seidl T.:
DUSC: Dimensionality Unbiased Subspace Clustering
Proc. IEEE International Conference on Data Mining (ICDM 2007), Omaha, Nebraska, USA P.409-414 (2007) (acceptance rate 19%)
[ICDM 2007]
Müller E.:
Density-based clustering in arbitrary subspaces
Proc. Workshop on Nature-inspired Methods for Local Pattern Detection (NiLOP 2007) in conjunction with NiSIS 2007, St. Julians, Malta (2007)
Assent I., Krieger R., Müller E., Steffens A., Seidl T.:
Evolutionary Subspace Search in biologically-inspired Optimal Niches
Proc. Annual Symposium on Nature inspired Smart Information Systems (NiSIS 2007), St Julians, Malta (2007)
[http://www.nisis.risk-technologies.com/]
Assent I., Krieger R., Müller E., Seidl T.:
Removing Dimensionality Bias in Density-based Subspace Clustering
Abstract in Dutch-Belgian Data Base Day (DBDBD 2007), Eindhoven, NL (2007)
[DBDBD 2007]
Assent I., Krieger R., Müller E., Seidl T.:
Subspace outlier mining in large multimedia databases
In: M. Berthold, K. Morik, A. Siebes(eds.): Parallel Universes and Local Patterns, Dagstuhl Seminar 07181 (2007)
[Dagstuhl seminar homepage] [Dagstuhl DROPS Document]
Müller E.:
Subspace Clustering für die Analyse von CGH Daten
Studierendenprogramm at the 12th GI-conference on Databases, Technology and Web (BTW 2007), Aachen, Germany: 31-33 (2007)
[BTW 2007] [BTW Studierendenprogramm]

2006

Assent I., Krieger R., Steffens A., Seidl T.:
A Novel Biology inspired Model for Evolutionary Subspace Clustering
Proc. Annual Symposium on Nature inspired Smart Information Systems (NiSIS 2006), Puerto de la Cruz, Tenerife (2006)
[NiSIS Project]