Dr. rer. nat. Stephan Günnemann
Post-Doctoral Researcher at
Carnegie Mellon University

E-Mail: sguennem(a)cs.cmu.edu
Phone: +1 412-268-7669
Fax: +1 412-268-5576
Room: GHC 7223


Computer Science Department
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213
USA
 

Research Activities

News

Research Focus

Tutorials at international conferences, invited talks

Organization and Editorships

Program committee member

Reviewer for International Journals

External reviewer at international conferences

Software

KDD-SC: Subspace Clustering Extensions for Knowledge Discovery Frameworks
http://dme.rwth-aachen.de/KDD-SC/
Technical Report

 

About me

Research Background

Academic Honors and Awards

 

Teaching Activities

Lecturer of the courses

Exercises and assistance for the courses

Supervised Bachelor, Master and Diploma Thesis

 

Publications

2014

  • Miguel Araujo, Stephan Günnemann, Gonzalo Mateos and Christos Faloutsos Beyond Blocks: Hyperbolic Community Detection European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), 2014 (to appear)
  • Stephan Günnemann, Nikou Günnemann and Christos Faloutsos Detecting Anomalies in Dynamic Rating Data: A Robust Probabilistic Model for Rating Evolution ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2014 (to appear)
  • Stephan Günnemann, Ines Färber, Matthias Rüdiger and Thomas Seidl SMVC: Semi-Supervised Multi-View Clustering in Subspace Projections ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2014 (to appear)
  • Nikou Günnemann, Stephan Günnemann and Christos Faloutsos Robust Multivariate Autoregression for Anomaly Detection in Dynamic Product Ratings International World Wide Web Conference (WWW), 2014 [PDF]
  • Tobias Kötter, Stephan Günnemann, Christos Faloutsos and Michael Berthold Fault-tolerant Concept Detection in Information Networks Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2014
  • Miguel Araujo, Spiros Papadimitriou, Stephan Günnemann, Christos Faloutsos, Prithwish Basu, Ananthram Swami, Evangelos Papalexakis and Danai Koutra Com2: Fast Automatic Discovery of Temporal ('Comet') Communities (Best Student Paper Runner-Up Award) Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2014

2013

  • Stephan Günnemann and Christos Faloutsos Mixed Membership Subspace Clustering IEEE International Conference on Data Mining (ICDM), 2013 [PDF], [Supplementary material], [KDnuggets news]
  • Stephan Günnemann, Ines Färber, Sebastian Raubach and Thomas Seidl Spectral Subspace Clustering for Graphs with Feature Vectors IEEE International Conference on Data Mining (ICDM), 2013 [PDF], [Supplementary material]
  • Hardy Kremer, Stephan Günnemann, Arne Held and Thomas Seidl An Evaluation Framework for Temporal Subspace Clustering Approaches IEEE International Conference on Data Mining Workshops (ICDMW), 2013 [PDF], [Download page]
  • Stephan Günnemann, Ines Färber, Brigitte Boden and Thomas Seidl GAMer: A Synthesis of Subspace Clustering and Dense Subgraph Mining Knowledge and Information Systems (KAIS), 2013 (online first) [PDF], [Supplementary material]
  • Brigitte Boden, Stephan Günnemann, Holger Hoffmann and Thomas Seidl RMiCS: A Robust Approach for Mining Coherent Subgraphs in Edge-Labeled Multi-Layer Graphs International Conference on Scientific and Statistical Database Management (SSDBM), 2013 [PDF]
  • Hardy Kremer, Stephan Günnemann, Simon Wollwage and Thomas Seidl Nesting the Earth Mover's Distance for Effective Cluster Tracing International Conference on Scientific and Statistical Database Management (SSDBM), 2013 [PDF]
  • Stephan Günnemann, Brigitte Boden, Ines Färber and Thomas Seidl Efficient Mining of Combined Subspace and Subgraph Clusters in Graphs with Feature Vectors Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 261-275, 2013 [PDF], [Supplementary material]
  • Stephan Günnemann Subspace Clustering for Complex Data GI Conference on Database Systems for Business, Technology, and the Web (BTW), pp. 343-362, 2013 [PDF]
  • Jennifer H. Nguyen, Bo Hu, Stephan Günnemann and Martin Ester Finding Contexts of Social Influence in Online Social Networks (Student paper award) 7th Workshop on Social Network Mining and Analysis at ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2013 [PDF]
  • Geng Li, Stephan Günnemann and Mohammed J. Zaki Stochastic Subspace Search for Top-K Multi-View Clustering 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering at ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2013 [PDF]

2012

  • Stephan Günnemann, Phuong Dao, Mohsen Jamali and Martin Ester Assessing the Significance of Data Mining Results on Graphs with Feature Vectors (Invitation to special issue: ICDM Best papers) Proc. IEEE International Conference on Data Mining (ICDM 2012), Brussels, Belgium, 2012 [PDF]
  • Hardy Kremer, Stephan Günnemann, Arne Held and Thomas Seidl Effective and Robust Mining of Temporal Subspace Clusters Proc. IEEE International Conference on Data Mining (ICDM 2012), Brussels, Belgium, 2012 [PDF]
  • Stephan Günnemann, Ines Färber and Thomas Seidl 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, 2012 [PDF], [Supplementary material]
  • Stephan Günnemann, Ines Färber, Kittipat Virochsiri and Thomas Seidl 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, 2012 [PDF]
  • Brigitte Boden, Stephan Günnemann, Holger Hoffmann and Thomas Seidl 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, 2012 [PDF]
  • Stephan Günnemann, Brigitte Boden and Thomas Seidl Finding Density-Based Subspace Clusters in Graphs with Feature Vectors Data Mining and Knowledge Discovery Journal (DMKD), Vol. 25, Nr. 2, pp. 243-269, 2012 [PDF], [Supplementary material]
  • Stephan Günnemann, Hardy Kremer, Charlotte Laufkötter and Thomas Seidl Tracing Evolving Subspace Clusters in Temporal Climate Data Data Mining and Knowledge Discovery (DMKD), Vol. 24(2), pp. 387-410, 2012 [PDF]
  • Brigitte Boden, Stephan Günnemann and Thomas Seidl Tracing Clusters in Evolving Graphs with Node Attributes Proceedings of The 21st ACM Conference on Information and Knowledge Management (CIKM 2012), Maui, USA , 2012 [PDF]
  • Hardy Kremer, Stephan Günnemann, Arne Held and Thomas Seidl Mining of Temporal Coherent Subspace Clusters in Multivariate Time Series Databases Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 444-455, 2012 [PDF]
  • Stephan Günnemann, Brigitte Boden and Thomas Seidl 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, 2012 [PDF]
  • Stephan Günnemann Subspace Clustering for Complex Data Dissertation, Fakultät für Mathematik, Informatik und Naturwissenschaften, RWTH Aachen University., 2012 [PDF]
  • Stephan Günnemann, Hardy Kremer, Richard Musiol, Roman Haag and Thomas Seidl A Subspace Clustering Extension for the KNIME Data Mining Framework Proc. IEEE International Conference on Data Mining (ICDM 2012), Brussels, Belgium, 2012 [PDF], [Download page]
  • Emmanuel Müller, Stephan Günnemann, Ines Färber and Thomas Seidl Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data Tutorial at IEEE 28th International Conference on Data Engineering (ICDE), 2012 [PDF]
  • Emmanuel Müller, Stephan Günnemann, Ines Färber and Thomas Seidl Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data Tutorial at the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2012

2011

  • Stephan Günnemann, Emmanuel Müller, Sebastian Raubach and Thomas Seidl Flexible Fault Tolerant Subspace Clustering for Data with Missing Values IEEE International Conference on Data Mining (ICDM), pp. 231-240, 2011 [PDF], [Supplementary material]
  • Stephan Günnemann, Brigitte Boden and Thomas Seidl DB-CSC: A density-based approach for subspace clustering in graphs with feature vectors (Best paper award) European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp. 565-580, 2011 [PDF], [Supplementary material], [Extended version]
  • Stephan Günnemann, Ines Färber, Emmanuel Müller, Ira Assent and Thomas Seidl External Evaluation Measures for Subspace Clustering ACM Conference on Information and Knowledge Management (CIKM), pp. 1363-1372, 2011 [PDF]
  • Emmanuel Müller, Ira Assent, Stephan Günnemann and Thomas Seidl Scalable Density-Based Subspace Clustering ACM Conference on Information and Knowledge Management (CIKM), pp. 1077-1086, 2011 [PDF]
  • Stephan Günnemann, Hardy Kremer, Charlotte Laufkötter and Thomas Seidl Tracing Evolving Clusters by Subspace and Value Similarity Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 444-456, 2011 [PDF]
  • Stephan Günnemann, Hardy Kremer, Dominik Lenhard and Thomas Seidl Subspace Clustering for Indexing High Dimensional Data: A Main Memory Index based on Local Reductions and Individual Multi-Representations International Conference on Extending Database Technology (EDBT), pp. 237-248, 2011 [PDF]
  • Hardy Kremer, Stephan Günnemann, Anca Maria Ivanescu, Ira Assent and Thomas Seidl Efficient Processing of Multiple DTW Queries in Time Series Databases International Conference on Scientific and Statistical Database Management (SSDBM), pp. 150-167, 2011 [PDF]
  • Emmanuel Müller, Ira Assent, Stephan Günnemann, Patrick Gerwert, Matthias Hannen, Timm Jansen and Thomas Seidl A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases GI Conference on Database Systems for Business, Technology, and the Web (BTW), pp. 347-366, 2011 [PDF]
  • Stephan Günnemann, Hardy Kremer and Thomas Seidl An Extension of the PMML Standard to Subspace Clustering Models Workshop on Predictive Model Markup Language at ACM Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 48-53, 2011 [PDF]
  • Emmanuel Müller, Stephan Günnemann, Ira Assent and Thomas Seidl Proceedings of the 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings CEUR Workshop Proceedings , 2011 [Proceedings]
  • Emmanuel Müller, Stephan Günnemann, Ines Färber and Thomas Seidl Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data Tutorial at SIAM International Conference on Data Mining (SDM), 2011 [PDF]

2010

  • Stephan Günnemann, Ines Färber, Brigitte Boden and Thomas Seidl Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms IEEE International Conference on Data Mining (ICDM), pp. 845-850, 2010 [PDF], [Extended Version], [Supplementary material]
  • Stephan Günnemann, Hardy Kremer and Thomas Seidl Subspace Clustering for Uncertain Data SIAM International Conference on Data Mining (SDM), pp. 385-396, 2010 [PDF], [Supplementary material]
  • Stephan Günnemann and Thomas Seidl Subgraph Mining on Directed and Weighted Graphs Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 133-146, 2010 [PDF]
  • Philipp Kranen, Stephan Günnemann, Fries, S. and Thomas Seidl MC-Tree: Improving Bayesian Anytime Classification International Conference on Scientific and Statistical Database Management (SSDBM), pp. 252-269, 2010 [PDF]
  • Stephan Günnemann, Ines Färber, Hardy Kremer and Thomas Seidl CoDA: Interactive Cluster Based Concept Discovery PVLDB, Vol. 3(2), pp. 1633-1636, 2010 [PDF]
  • Ira Assent, Hardy Kremer, Stephan Günnemann and Thomas Seidl Pattern detector: fast detection of suspicious stream patterns for immediate reaction International Conference on Extending Database Technology (EDBT), pp. 709-712, 2010 [PDF]
  • Stephan Günnemann, Ines Färber, Emmanuel Müller and Thomas Seidl ASCLU: Alternative Subspace Clustering International Workshop on Discovering, Summarizing and Using Multiple Clusterings at ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2010 [PDF]
  • Ira Assent, Emmanuel Müller, Stephan Günnemann, Ralph Krieger and Thomas Seidl Less is More: Non-Redundant Subspace Clustering International Workshop on Discovering, Summarizing and Using Multiple Clusterings at ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2010 [PDF]
  • Ines Färber, Stephan Günnemann, Hans-Peter Kriegel, Peer Kröger, Emmanuel Müller, Erich Schubert, Thomas Seidl and Arthur Zimek On Using Class-Labels in Evaluation of Clusterings International Workshop on Discovering, Summarizing and Using Multiple Clusterings at ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2010 [PDF]
  • Stephan Günnemann, Hardy Kremer, Ines Färber and Thomas Seidl MCExplorer: Interactive Exploration of Multiple (Subspace) Clustering Solutions IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1387-1390, 2010 [PDF]
  • Hardy Kremer, Stephan Günnemann and Thomas Seidl Detecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing Techniques IEEE International Conference on Data Mining Workshops (ICDMW), pp. 96-97, 2010 [PDF]
  • Emmanuel Müller, Stephan Günnemann, Ines Färber and Thomas Seidl Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data Tutorial at IEEE International Conference on Data Mining (ICDM), pp. 1220, 2010 [PDF]

2009

  • Stephan Günnemann, Emmanuel Müller, Ines Färber and Thomas Seidl Detection of orthogonal concepts in subspaces of high dimensional data ACM Conference on Information and Knowledge Management (CIKM), pp. 1317-1326, 2009 [PDF]
  • Emmanuel Müller, Ira Assent, Stephan Günnemann, Ralph Krieger and Thomas Seidl Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data IEEE International Conference on Data Mining (ICDM), pp. 377-386, 2009 [PDF], [Supplementary material]
  • Emmanuel Müller, Ira Assent, Ralph Krieger, Stephan Günnemann and Thomas Seidl DensEst: Density Estimation for Data Mining in High Dimensional Spaces SIAM International Conference on Data Mining (SDM), pp. 173-184, 2009 [PDF]
  • Emmanuel Müller, Stephan Günnemann, Ira Assent and Thomas Seidl Evaluating Clustering in Subspace Projections of High Dimensional Data PVLDB, Vol. 2(1), pp. 1270-1281, 2009 [PDF], [Supplementary material]
  • Ira Assent, Stephan Günnemann, Hardy Kremer and Thomas Seidl High-Dimensional Indexing for Multimedia Features GI Conference on Database Systems for Business, Technology, and the Web (BTW), pp. 187-206, 2009 [PDF]
  • Emmanuel Müller, Ira Assent, Stephan Günnemann, Timm Jansen and Thomas Seidl OpenSubspace: An Open Source Framework for Evaluation and Exploration of Subspace Clustering Algorithms in WEKA Open Source in Data Mining Workshop at Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 2-13, 2009 [PDF]

(c) Stephan Günnemann, Stephan Guennemann, Stephan Gunnemann