Publikationen

2018

  • Aleksandar Bojchevski, Stephan Günnemann
    Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure
    AAAI Conference on Artificial Intelligence, 2018 (to appear)

2017

  • Aleksandar Bojchevski, Yves Matkovic, Stephan Günnemann
    Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 737-746, 2017
    [Supplementary Material]
  • Dhivya Eswaran, Stephan Günnemann, Christos Faloutsos
    The Power of Certainty: A Dirichlet-Multinomial Model for Belief Propagation
    SIAM International Conference on Data Mining (SDM), pp. 144-152, 2017
  • Dhivya Eswaran, Stephan Günnemann, Christos Faloutsos, Disha Makhija, Mohit Kumar
    ZooBP: Belief Propagation for Heterogeneous Networks
    International Conference on Very Large Data Bases, PVLDB 10(5): 625-636 (2017)
    [PDF]
  • Manuel Then, Timo Kersten, Stephan Günnemann, Alfons Kemper, Thomas Neumann
    Automatic Algorithm Transformation for Efficient Multi-Snapshot Analytics on Temporal Graphs
    International Conference on Very Large Data Bases, PVLDB 10(8): 877-888 (2017)
  • Nina Hubig, Philip Fengler, Andreas Züfle, Ruixin Yang, Stephan Günnemann
    Detection and Prediction of Natural Hazards using Large-Scale Environmental Data
    International Symposium on Spatial and Temporal Databases (SSTD), pp. 300-316, 2017 
  • Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl
    MiMAG: Mining Coherent Subgraphs in Multi-Layer Graphs with Edge Labels
    Knowledge and Information Systems (KAIS), pp. 417-446, 2017
    [Supplementary material]
  • Linnea Passing, Manuel Then, Nina Hubig, Harald Lang, Michael Schreier, Stephan Günnemann, Alfons Kemper, Thomas Neumann
    SQL- and Operator-centric Data Analytics in Relational Main-Memory Databases
    International Conference on Extending Database Technology (EDBT), pp. 84-95, 2017
  • Manuel Then, Stephan Günnemann, Alfons Kemper, Thomas Neumann 
    Efficient Batched Distance, Closeness and Betweenness Centrality Computation in Unweighted and Weighted Graphs
    Datenbank-Spektrum, 17(2): 169-182 (2017)
  • Manuel Then, Stephan Günnemann, Alfons Kemper, Thomas Neumann
    Efficient Batched Distance and Centrality Computation in Unweighted and Weighted Graphs
    GI Conference on Database Systems for Business, Technology, and the Web (BTW), pp. 247-266, 2017
  • Stephan Günnemann
    Machine Learning Meets Databases
    Datenbank-Spektrum, pp. 77-83, 2017, invited paper
    [PDF]

2016

  • Saskia Metzler, Stephan Günnemann, Pauli Miettinen
    Hyperbolae Are No Hyperbole: Modelling Communities That Are Not Cliques
    IEEE International Conference on Data Mining (ICDM), pp. 330-339, 2016.
  • Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Günnemann, Disha Makhija, Mohit Kumar, Christos Faloutsos,
    EdgeCentric: Anomaly Detection in Edge-Attributed Networks
    IEEE
    International Conference on Data Mining Workshops (ICDMW), pp. 327-334, 2016.
  • Subhabrata Mukherjee, Stephan Günnemann, Gerhard Weikum
    Continuous Experience-aware Language Model
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 1075-1084, 2016.
  • Bryan Hooi, Neil Shah, Alex Beutel, Stephan Günnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos
    BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
    SIAM
    International Conference on Data Mining (SDM), pp. 495-503, 2016.
  • Miguel Araujo, Stephan Günnemann, Spiros Papadimitriou, Christos Faloutsos, Prithwish Basu, Ananthram Swami, Evangelos Papalexakis and Danai Koutra
    Discovery ofcometcommunities in temporal and labeled graphs (Com2)
    Knowledge and Information Systems (KAIS), pp. 657-677, 2016

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2015

  • Tobias Kötter, Stephan Günnemann, Christos Faloutsos, and Michael R. Berthold
    Automatic taxonomy extraction from bipartite graphs
    (Invitation to special issue: ICDM Best papers)
    IEEE International Conference on Data Mining (ICDM), pages 221–230, 2015.
  • Wolfgang Gatterbauer, Stephan Günnemann, Danai Koutra, Christos Faloutsos
    Linearized and Single-Pass Belief Propagation
    PVLDB, Vol. 8(5), pp. 581-592, 2015
  • Tobias Kötter, Stephan Günnemann, Michael Berthold, and Christos Faloutsos
    Extracting Taxonomies from Bipartite Graphs
    International World Wide Web Conference (WWW), pp. 51-52, 2015
  • Jay-Yoon Lee, Manzil Zaheer, Stephan Günnemann, Alexander J. Smola
    Preferential Attachment in Graphs with Affinities
    International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 571-580, 2015
  • Manuel Then, Linnea Passing, Nina Hubig, Stephan Günnemann, Alfons Kemper, and Thomas Neumann
    Effiziente Integration von Data- und Graph-Mining-Algorithmen in relationale Datenbanksysteme 
    LWA 2015, Special interest group database systems, pages 45–49, 2015.
  • Emmanuel Müller, Ira Assent, Stephan Günnemann, Thomas Seidl, Jennifer Dy
    Editorial: MultiClust Special Issue on Discovering, Summarizing and Using Multiple Clusterings
    Machine Learning Journal (MLJ), Vol. 98(1-2), pp. 1-5, 2015
    [PDF]
     

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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), pp. 50-65, 2014
    [PDF], [Supplementary material]
  • 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), pp. 841-850, 2014
    [PDF]
  • 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), pp. 253-262, 2014
    [PDF], [Supplementary material]
  • Nikou Günnemann, Stephan Günnemann and Christos Faloutsos
    Robust Multivariate Autoregression for Anomaly Detection in Dynamic Product Ratings
    International World Wide Web Conference (WWW), pp. 361-372, 2014
    [PDF]
  • Tobias Kötter, Stephan Günnemann, Christos Faloutsos and Michael BertholdFault-tolerant Concept Detection in Information Networks
    Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 410-421, 2014
    [PDF], [Supplementary material]
  • 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), pp. 271-283, 2014
    [PDF], [Supplementary material]
  • Stephan Günnemann, Ines Färber, Brigitte Boden and Thomas Seidl
    GAMer: A Synthesis of Subspace Clustering and Dense Subgraph Mining
    Knowledge and Information Systems (KAIS), Vol. 40(2), pp. 243-278, 2014
    [PDF], [Supplementary material]
     

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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 SeidlAn Evaluation Framework for Temporal Subspace Clustering ApproachesIEEE International Conference on Data Mining Workshops (ICDMW), 2013
    [PDF], [Download page]
  • 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 GünnemannSubspace 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]
     

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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], [Supplementary material]
  • 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
     

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2011

  • Stephan Günnemann, Emmanuel Müller, Sebastian Raubach and Thomas SeidlFlexible Fault Tolerant Subspace Clustering for Data with Missing ValuesIEEE International Conference on Data Mining (ICDM), pp. 231-240, 2011[PDF], [Supplementary material]
  • Stephan Günnemann, Brigitte Boden and Thomas SeidlDB-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 SeidlExternal Evaluation Measures for Subspace ClusteringACM Conference on Information and Knowledge Management (CIKM), pp. 1363-1372, 2011[PDF]
  • Emmanuel Müller, Ira Assent, Stephan Günnemann and Thomas SeidlScalable Density-Based Subspace ClusteringACM Conference on Information and Knowledge Management (CIKM), pp. 1077-1086, 2011[PDF]
  • Stephan Günnemann, Hardy Kremer, Charlotte Laufkötter and Thomas SeidlTracing Evolving Clusters by Subspace and Value SimilarityPacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 444-456, 2011[PDF]
  • Stephan Günnemann, Hardy Kremer, Dominik Lenhard and Thomas SeidlSubspace Clustering for Indexing High Dimensional Data: A Main Memory Index based on Local Reductions and Individual Multi-RepresentationsInternational Conference on Extending Database Technology (EDBT), pp. 237-248, 2011[PDF]
  • Hardy Kremer, Stephan Günnemann, Anca Maria Ivanescu, Ira Assent and Thomas SeidlEfficient Processing of Multiple DTW Queries in Time Series DatabasesInternational 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 SeidlA Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional DatabasesGI Conference on Database Systems for Business, Technology, and the Web (BTW), pp. 347-366, 2011[PDF]
  • Stephan Günnemann, Hardy Kremer and Thomas SeidlAn Extension of the PMML Standard to Subspace Clustering ModelsWorkshop 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 SeidlProceedings of the 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple ClusteringsCEUR Workshop Proceedings , 2011[Proceedings]
  • Emmanuel Müller, Stephan Günnemann, Ines Färber and Thomas SeidlDiscovering Multiple Clustering Solutions: Grouping Objects in Different Views of the DataTutorial at SIAM International Conference on Data Mining (SDM), 2011
    [PDF]
     

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2010

  • Stephan Günnemann, Ines Färber, Brigitte Boden and Thomas SeidlSubspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two ParadigmsIEEE International Conference on Data Mining (ICDM), pp. 845-850, 2010[PDF], [Extended Version], [Supplementary material]
  • Stephan Günnemann, Hardy Kremer and Thomas SeidlSubspace Clustering for Uncertain DataSIAM International Conference on Data Mining (SDM), pp. 385-396, 2010[PDF], [Supplementary material]
  • Stephan Günnemann and Thomas SeidlSubgraph Mining on Directed and Weighted GraphsPacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 133-146, 2010[PDF]
  • Philipp Kranen, Stephan Günnemann, Fries, S. and Thomas SeidlMC-Tree: Improving Bayesian Anytime ClassificationInternational Conference on Scientific and Statistical Database Management (SSDBM), pp. 252-269, 2010[PDF]
  • Stephan Günnemann, Ines Färber, Hardy Kremer and Thomas SeidlCoDA: Interactive Cluster Based Concept DiscoveryPVLDB, Vol. 3(2), pp. 1633-1636, 2010[PDF]
  • Ira Assent, Hardy Kremer, Stephan Günnemann and Thomas SeidlPattern detector: fast detection of suspicious stream patterns for immediate reactionInternational Conference on Extending Database Technology (EDBT), pp. 709-712, 2010[PDF]
  • Stephan Günnemann, Ines Färber, Emmanuel Müller and Thomas SeidlASCLU: Alternative Subspace ClusteringInternational 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 SeidlLess is More: Non-Redundant Subspace ClusteringInternational 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 ZimekOn Using Class-Labels in Evaluation of ClusteringsInternational 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 SeidlMCExplorer: Interactive Exploration of Multiple (Subspace) Clustering SolutionsIEEE International Conference on Data Mining Workshops (ICDMW), pp. 1387-1390, 2010[PDF]
  • Hardy Kremer, Stephan Günnemann and Thomas SeidlDetecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing TechniquesIEEE International Conference on Data Mining Workshops (ICDMW), pp. 96-97, 2010[PDF]
  • Emmanuel Müller, Stephan Günnemann, Ines Färber and Thomas SeidlDiscovering Multiple Clustering Solutions: Grouping Objects in Different Views of the DataTutorial at IEEE International Conference on Data Mining (ICDM), pp. 1220, 2010[PDF]
     

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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 SeidlRelevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional DataIEEE International Conference on Data Mining (ICDM), pp. 377-386, 2009[PDF], [Supplementary material]
  • Emmanuel Müller, Ira Assent, Ralph Krieger, Stephan Günnemann and Thomas SeidlDensEst: Density Estimation for Data Mining in High Dimensional SpacesSIAM International Conference on Data Mining (SDM), pp. 173-184, 2009[PDF]
  • Emmanuel Müller, Stephan Günnemann, Ira Assent and Thomas SeidlEvaluating Clustering in Subspace Projections of High Dimensional DataPVLDB, 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]

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