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ORCIDEmmanuel Müller
Emmanuel Alexander Müller
Person information
- affiliation: Technical University of Dortmund, Germany
- affiliation (former): Hasso Plattner Institute, Potsdam, Germany
- affiliation (former): Karlsruhe Institute of Technology, IPD, Germany
- affiliation (former): RWTH Aachen University, Germany
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2020 – today
- 2024
[j12]Bin Li
, Shubham Gupta, Emmanuel Müller:
State-transition-aware anomaly detection under concept drifts. Data Knowl. Eng. 154: 102365 (2024)- [c99]Chiara Balestra, Antonio Ferrara, Emmanuel Müller:
FairMC Fair-Markov Chain Rank Aggregation Methods. DaWaK 2024: 315-321 - [c98]Mohammad-Sahadet Hossain, Mohammad Sakhawat Hossain, Simon Klüttermann, Emmanuel Müller:
Evaluating Anomaly Detection Algorithms: A Multi-Metric Analysis Across Variable Class Imbalances. IJCNN 2024: 1-7 - [c97]Simon Klüttermann, Jérôme Rutinowski, Emmanuel Müller:
The Phenomenon of Correlated Representations in Contrastive Learning. IJCNN 2024: 1-8 - [c96]Vikas Kumar, Vishesh Srivastava, Sadia Mahjabin, Arindam Pal, Simon Klüttermann, Emmanuel Müller:
Autoencoder Optimization for Anomaly Detection: A Comparative Study with Shallow Algorithms. IJCNN 2024: 1-8 - [c95]Bin Li, Emmanuel Müller:
Cohesive Explanation for Time Series Prediction. IJCNN 2024: 1-9 - [c94]Simon Klüttermann, Chiara Balestra, Emmanuel Müller:
On the Efficient Explanation of Outlier Detection Ensembles Through Shapley Values. PAKDD (3) 2024: 43-55 - [c93]Jérôme Rutinowski, Simon Klüttermann, Jan Endendyk, Christopher Reining, Emmanuel Müller:
Benchmarking Trust: A Metric for Trustworthy Machine Learning. xAI (1) 2024: 287-307 - [e4]Zahraa S. Abdallah, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier, Matthias Jakobs, Emmanuel Müller, Maximilian Muschalik, Panagiotis Papapetrou, Amal Saadallah, George Tzagkarakis:
Proceedings of the Workshop on Explainable AI for Time Series and Data Streams (TempXAI 2024) co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2024), Vilnius, Lithuania, September 9th, 2024. CEUR Workshop Proceedings 3761, CEUR-WS.org 2024 [contents] - [i25]Simon Klüttermann, Jérôme Rutinowski, Anh Nguyen, Britta Grimme, Moritz Roidl, Emmanuel Müller:
On the Effectiveness of Heterogeneous Ensemble Methods for Re-identification. CoRR abs/2403.12606 (2024) - [i24]Simon Klüttermann, Emmanuel Müller:
About Test-time training for outlier detection. CoRR abs/2404.03495 (2024) - [i23]Chiara Balestra, Andreas Mayr, Emmanuel Müller:
Ranking evaluation metrics from a group-theoretic perspective. CoRR abs/2408.16009 (2024) - [i22]Minjae Ok, Simon Klüttermann, Emmanuel Müller:
Exploring the Impact of Outlier Variability on Anomaly Detection Evaluation Metrics. CoRR abs/2409.15986 (2024) - 2023
- [j11]Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller:
Graph Clustering with Graph Neural Networks. J. Mach. Learn. Res. 24: 127:1-127:21 (2023) - [c92]Magdalena Wischnewski, Nicole C. Krämer, Emmanuel Müller:
Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions. CHI 2023: 755:1-755:16 - [c91]Arn Baudzus, Bin Li, Adnane Jadid, Emmanuel Müller:
On Model Performance Estimation in Time Series Anomaly Detection. CIIS 2023: 106-117 - [c90]Bin Li, Emmanuel Müller:
State-Transition-Aware Anomaly Detection Under Concept Drifts. DaWaK 2023: 49-63 - [c89]Gernot Schmitz, Daniel Wilmes, Alexander Gerharz, Daniel Horn, Emmanuel Müller:
Contextual Shift Method (CSM). DaWaK 2023: 101-106 - [c88]Lara Kuhlmann, Daniel Wilmes, Emmanuel Müller, Markus Pauly, Daniel Horn:
RODD: Robust Outlier Detection in Data Cubes. DaWaK 2023: 325-339 - [c87]Chiara Balestra, Bin Li, Emmanuel Müller:
slidSHAPs - sliding Shapley Values for correlation-based change detection in time series. DSAA 2023: 1-10 - [c86]Carina Newen, Emmanuel Müller:
On the Independence of Adversarial Transferability to Topological Changes in the Dataset. DSAA 2023: 1-8 - [c85]Simon Klüttermann, Jérôme Rutinowski, Anh Nguyen, Christopher Reining, Moritz Roidl, Emmanuel Müller:
On Graph Representation based Re-Identification - A Proof of Concept. ICDM (Workshops) 2023: 1097-1104 - [c84]Simon Klüttermann, Emmanuel Müller:
Evaluating and Comparing Heterogeneous Ensemble Methods for Unsupervised Anomaly Detection. IJCNN 2023: 1-8 - [c83]Bin Li, Emmanuel Müller:
Contrastive Time Series Anomaly Detection by Temporal Transformations. IJCNN 2023: 1-8 - [c82]Arn Baudzus, Bin Li, Adnane Jadid, Emmanuel Müller:
The Good, The Bad, and The Average: Benchmarking of Reconstruction Based Multivariate Time Series Anomaly Detection. ECML/PKDD (7) 2023: 356-360 - [i21]Lara Kuhlmann, Daniel Wilmes, Emmanuel Müller, Markus Pauly, Daniel Horn:
RODD: Robust Outlier Detection in Data Cubes. CoRR abs/2303.08193 (2023) - [i20]Simon Lutz, Florian Wittbold, Simon Dierl, Benedikt Böing, Falk Howar, Barbara König, Emmanuel Müller, Daniel Neider:
Interpretable Anomaly Detection via Discrete Optimization. CoRR abs/2303.14111 (2023) - [i19]Bin Li, Carsten Jentsch, Emmanuel Müller:
Prototypes as Explanation for Time Series Anomaly Detection. CoRR abs/2307.01601 (2023) - [i18]Chiara Balestra, Carlo Maj, Emmanuel Müller, Andreas Mayr:
Redundancy-aware unsupervised rankings for collections of gene sets. CoRR abs/2307.16182 (2023) - [i17]Chiara Balestra, Bin Li, Emmanuel Müller:
On the Consistency and Robustness of Saliency Explanations for Time Series Classification. CoRR abs/2309.01457 (2023) - 2022
- [c81]Benedikt Böing, Falk Howar, Jelle Hüntelmann, Emmanuel Müller, Richard Stewing:
Neural Network Verification with DSE. OVERLAY@AI*IA 2022: 1-6 - [c80]Chiara Balestra, Florian Huber, Andreas Mayr, Emmanuel Müller:
Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data. DaWaK 2022: 97-111 - [c79]Benedikt Böing, Emmanuel Müller:
On Training and Verifying Robust Autoencoders. DSAA 2022: 1-10 - [c78]Carina Newen, Emmanuel Müller:
Unsupervised DeepView: Global Explainability of Uncertainties for High Dimensional Data. ICKG 2022: 196-202 - [c77]Carina Newen, Emmanuel Müller:
Unsupervised DeepView: Global Uncertainty Visualization for High Dimensional Data. ICDM (Workshops) 2022: 1-8 - [c76]Daniil Kaminskyi, Bin Li, Emmanuel Müller:
Reconstruction-based unsupervised drift detection over multivariate streaming data. ICDM (Workshops) 2022: 807-813 - [c75]Benedikt Böing, Simon Klüttermann, Emmanuel Müller:
Post-Robustifying Deep Anomaly Detection Ensembles by Model Selection. ICDM 2022: 861-866 - [c74]Simon Klüttermann, Jérôme Rutinowski, Christopher Reining, Moritz Roidl, Emmanuel Müller:
Towards Graph Representation based Re-Identification of Chipwood Pallet Blocks. ICMLA 2022: 1543-1550 - [c73]Benedikt Tobias Müller, Marvin Ender, Jan Erik Swiadek, Mengcheng Jin, Simon Winkel, Dominik Niedziela, Bin Li, Jelle Hüntelmann, Emmanuel Müller:
ADEPT: Anomaly Detection, Explanation and Processing for Time Series with a Focus on Energy Consumption Data. ECML/PKDD (6) 2022: 622-626 - [c72]Bin Li, Emmanuel Müller:
STAD: State-Transition-Aware Anomaly Detection Under Concept Drifts. OLUD@WCCI 2022 - [i16]Chiara Balestra, Florian Huber, Andreas Mayr, Emmanuel Müller:
Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data. CoRR abs/2205.09060 (2022) - [i15]Chiara Balestra, Carlo Maj, Emmanuel Müller, Andreas Mayr:
Redundancy-aware unsupervised ranking based on game theory - application to gene enrichment analysis. CoRR abs/2207.12184 (2022) - [i14]Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Emmanuel Müller:
Spectral Graph Complexity. CoRR abs/2211.01434 (2022) - 2021
- [j10]Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Ivan V. Oseledets, Emmanuel Müller:
FREDE: Anytime Graph Embeddings. Proc. VLDB Endow. 14(6): 1102-1110 (2021) - [c71]Benedikt Böing, Rajarshi Roy, Daniel Neider, Emmanuel Müller:
QUGA - Quality Guarantees for Autoencoders. OVERLAY@GandALF 2021: 103-107 - [c70]Erik Scharwächter, Jonathan Lennartz, Emmanuel Müller:
Differentiable Segmentation of Sequences. ICLR 2021 - 2020
- [c69]Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, Marius Kloft:
Deep Semi-Supervised Anomaly Detection. ICLR 2020 - [c68]Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Ivan V. Oseledets, Emmanuel Müller:
The Shape of Data: Intrinsic Distance for Data Distributions. ICLR 2020 - [c67]Benedikt Böing, Rajarshi Roy, Emmanuel Müller, Daniel Neider:
Quality Guarantees for Autoencoders via Unsupervised Adversarial Attacks. ECML/PKDD (2) 2020: 206-222 - [c66]Erik Scharwächter, Emmanuel Müller:
Two-Sample Testing for Event Impacts in Time Series. SDM 2020: 10-18 - [i13]Erik Scharwächter, Emmanuel Müller:
Does Terrorism Trigger Online Hate Speech? On the Association of Events and Time Series. CoRR abs/2004.14733 (2020) - [i12]Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Ivan V. Oseledets, Emmanuel Müller:
FREDE: Linear-Space Anytime Graph Embeddings. CoRR abs/2006.04746 (2020) - [i11]Erik Scharwächter, Jonathan Lennartz, Emmanuel Müller:
Differentiable Segmentation of Sequences. CoRR abs/2006.13105 (2020) - [i10]Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller:
Graph Clustering with Graph Neural Networks. CoRR abs/2006.16904 (2020) - [i9]Erik Scharwächter, Emmanuel Müller:
Statistical Evaluation of Anomaly Detectors for Sequences. CoRR abs/2008.05788 (2020)
2010 – 2019
- 2019
- [c65]Tara Safavi, Caleb Belth, Lukas Faber, Davide Mottin, Emmanuel Müller, Danai Koutra:
Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket. ICDM 2019: 528-537 - [c64]Nikita Klyuchnikov, Davide Mottin, Georgia Koutrika, Emmanuel Müller, Panagiotis Karras:
Figuring out the User in a Few Steps: Bayesian Multifidelity Active Search with Cokriging. KDD 2019: 686-695 - [c63]Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Emmanuel Müller:
Spectral Graph Complexity. WWW (Companion Volume) 2019: 308-309 - [i8]Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Ivan V. Oseledets, Emmanuel Müller:
Intrinsic Multi-scale Evaluation of Generative Models. CoRR abs/1905.11141 (2019) - [i7]Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, Marius Kloft:
Deep Semi-Supervised Anomaly Detection. CoRR abs/1906.02694 (2019) - 2018
- [c62]Arvind Kumar Shekar, Marcus Pappik, Patricia Iglesias Sánchez, Emmanuel Müller:
Selection of Relevant and Non-Redundant Multivariate Ordinal Patterns for Time Series Classification. DS 2018: 224-240 - [c61]Davide Mottin, Bastian Grasnick, Axel Kroschk, Patrick Siegler, Emmanuel Müller:
Notable Characteristics Search through Knowledge Graphs. EDBT 2018: 429-432 - [c60]Lukas Ruff, Nico Görnitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Robert A. Vandermeulen, Alexander Binder, Emmanuel Müller, Marius Kloft:
Deep One-Class Classification. ICML 2018: 4390-4399 - [c59]Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Emmanuel Müller:
NetLSD: Hearing the Shape of a Graph. KDD 2018: 2347-2356 - [c58]Erik Scharwächter, Fabian Geier, Lukas Faber, Emmanuel Müller:
Low Redundancy Estimation of Correlation Matrices for Time Series Using Triangular Bounds. PAKDD (2) 2018: 458-470 - [c57]Freya Behrens, Sebastian Bischoff, Pius Ladenburger, Julius Rückin, Laurenz Seidel, Fabian Stolp, Michael Vaichenker, Adrian Ziegler, Davide Mottin, Fatemeh Aghaei, Emmanuel Müller, Martin Preusse, Nikola Müller, Michael Hunger:
MetaExp: Interactive Explanation and Exploration of Large Knowledge Graphs. WWW (Companion Volume) 2018: 199-202 - [c56]Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller:
VERSE: Versatile Graph Embeddings from Similarity Measures. WWW 2018: 539-548 - [i6]Davide Mottin, Bastian Grasnick, Axel Kroschk, Patrick Siegler, Emmanuel Müller:
Notable Characteristics Search through Knowledge Graphs. CoRR abs/1802.04060 (2018) - [i5]Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller:
VERSE: Versatile Graph Embeddings from Similarity Measures. CoRR abs/1803.04742 (2018) - [i4]Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Emmanuel Müller:
NetLSD: Hearing the Shape of a Graph. CoRR abs/1805.10712 (2018) - [i3]Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Emmanuel Müller:
SGR: Self-Supervised Spectral Graph Representation Learning. CoRR abs/1811.06237 (2018) - 2017
- [c55]Fabian Maschler, Fabian Geier, Bodo Bookhagen, Emmanuel Müller:
Locality-Based Graph Clustering of Spatially Embedded Time Series. COMPLEX NETWORKS 2017: 719-730 - [c54]Arvind Kumar Shekar, Patricia Iglesias Sánchez, Emmanuel Müller:
Diverse Selection of Feature Subsets for Ensemble Regression. DaWaK 2017: 259-273 - [c53]Arvind Kumar Shekar, Tom Bocklisch, Patricia Iglesias Sánchez, Christoph Nikolas Straehle, Emmanuel Müller:
Including Multi-feature Interactions and Redundancy for Feature Ranking in Mixed Datasets. ECML/PKDD (1) 2017: 239-255 - [c52]Louis Kirsch, Niklas Riekenbrauck, Daniel Thevessen, Marcus Pappik, Axel Stebner, Julius Kunze, Alexander Meissner, Arvind Kumar Shekar, Emmanuel Müller:
Framework for Exploring and Understanding Multivariate Correlations. ECML/PKDD (3) 2017: 404-408 - [c51]Davide Mottin, Emmanuel Müller:
Graph Exploration: From Users to Large Graphs. SIGMOD Conference 2017: 1737-1740 - 2016
- [c50]Thomas Van Brussel, Emmanuel Müller, Bart Goethals:
Discovering Overlapping Quantitative Associations by Density-Based Mining of Relevant Attributes. FoIKS 2016: 131-148 - [c49]Andranik Khachatryan, Emmanuel Müller, Klemens Böhm, Christian Stier:
Improving accuracy and robustness of self-tuning histograms by subspace clustering. ICDE 2016: 1544-1545 - [c48]Erik Scharwächter, Emmanuel Müller, Jonathan F. Donges, Marwan Hassani, Thomas Seidl:
Detecting Change Processes in Dynamic Networks by Frequent Graph Evolution Rule Mining. ICDM 2016: 1191-1196 - [e3]Ralf Krestel, Davide Mottin, Emmanuel Müller:
Proceedings of the Conference "Lernen, Wissen, Daten, Analysen", Potsdam, Germany, September 12-14, 2016. CEUR Workshop Proceedings 1670, CEUR-WS.org 2016 [contents] - 2015
- [j9]Hoang Vu Nguyen, Emmanuel Müller, Jilles Vreeken, Klemens Böhm:
Erratum to: Unsupervised interaction-preserving discretization of multivariate data. Data Min. Knowl. Discov. 29(1): 296-297 (2015) - [j8]Emmanuel Müller, Ira Assent, Stephan Günnemann, Thomas Seidl, Jennifer G. Dy:
MultiClust special issue on discovering, summarizing and using multiple clusterings. Mach. Learn. 98(1-2): 1-5 (2015) - [j7]Andranik Khachatryan, Emmanuel Müller, Christian Stier, Klemens Böhm:
Improving Accuracy and Robustness of Self-Tuning Histograms by Subspace Clustering. IEEE Trans. Knowl. Data Eng. 27(9): 2377-2389 (2015) - [c47]Thibault Sellam, Emmanuel Müller, Martin L. Kersten:
Semi-Automated Exploration of Data Warehouses. CIKM 2015: 1321-1330 - [c46]Emin Aksehirli, Bart Goethals, Emmanuel Müller:
Efficient Cluster Detection by Ordered Neighborhoods. DaWaK 2015: 15-27 - [c45]Hoang Vu Nguyen, Klemens Böhm, Florian Becker, Bertrand Goldman, Georg Hinkel, Emmanuel Müller:
Identifying User Interests within the Data Space - a Case Study with SkyServer. EDBT 2015: 641-652 - [c44]Patricia Iglesias Sánchez, Emmanuel Müller, Uwe Leo Korn, Klemens Böhm, Andrea Kappes, Tanja Hartmann, Dorothea Wagner:
Efficient Algorithms for a Robust Modularity-Driven Clustering of Attributed Graphs. SDM 2015: 100-108 - [c43]Emmanuel Müller:
Keynote abstract: Subspace search for community detection and community outlier mining in attributed graphs. SMAP 2015: xvii - [c42]Fabian Keller, Emmanuel Müller, Klemens Böhm:
Estimating mutual information on data streams. SSDBM 2015: 3:1-3:12 - 2014
- [j6]Hoang Vu Nguyen, Emmanuel Müller, Klemens Böhm:
A Near-Linear Time Subspace Search Scheme for Unsupervised Selection of Correlated Features. Big Data Res. 1: 37-51 (2014) - [j5]Hoang Vu Nguyen, Emmanuel Müller, Jilles Vreeken, Klemens Böhm:
Unsupervised interaction-preserving discretization of multivariate data. Data Min. Knowl. Discov. 28(5-6): 1366-1397 (2014) - [c41]Hoang Vu Nguyen, Emmanuel Müller, Jilles Vreeken, Pavel Efros, Klemens Böhm:
Multivariate Maximal Correlation Analysis. ICML 2014: 775-783 - [c40]Bryan Perozzi, Leman Akoglu, Patricia Iglesias Sánchez, Emmanuel Müller:
Focused clustering and outlier detection in large attributed graphs. KDD 2014: 1346-1355 - [c39]Emmanuel Müller:
Subspace Search for Community Detection and Community Outlier Mining in Attributed Graphs. LWA 2014: 109-110 - [c38]Patricia Iglesias Sánchez, Emmanuel Müller, Oretta Irmler, Klemens Böhm:
Local context selection for outlier ranking in graphs with multiple numeric node attributes. SSDBM 2014: 16:1-16:12 - [c37]Hoang Vu Nguyen, Emmanuel Müller, Periklis Andritsos, Klemens Böhm:
Detecting correlated columns in relational databases with mixed data types. SSDBM 2014: 30:1-30:12 - 2013
- [c36]Hoang Vu Nguyen, Emmanuel Müller, Klemens Böhm:
4S: Scalable subspace search scheme overcoming traditional Apriori processing. IEEE BigData 2013: 359-367 - [c35]Fabian Keller, Emmanuel Müller, Andreas Wixler, Klemens Böhm:
Flexible and adaptive subspace search for outlier analysis. CIKM 2013: 1381-1390 - [c34]Emmanuel Müller, Patricia Iglesias Sánchez, Yvonne Mülle, Klemens Böhm:
Ranking outlier nodes in subspaces of attributed graphs. ICDE Workshops 2013: 216-222 - [c33]Patricia Iglesias Sánchez, Emmanuel Müller, Fabian Laforet, Fabian Keller, Klemens Böhm:
Statistical Selection of Congruent Subspaces for Mining Attributed Graphs. ICDM 2013: 647-656 - [c32]Emin Aksehirli, Bart Goethals, Emmanuel Müller, Jilles Vreeken:
Cartification: A Neighborhood Preserving Transformation for Mining High Dimensional Data. ICDM 2013: 937-942 - [c31]Emmanuel Müller:
Flexible Subspace Search for Outlier Detection and Description. LWA 2013: 121 - [c30]Klemens Böhm, Fabian Keller, Emmanuel Müller, Hoang Vu Nguyen, Jilles Vreeken:
CMI: An Information-Theoretic Contrast Measure for Enhancing Subspace Cluster and Outlier Detection. SDM 2013: 198-206 - 2012
- [c29]Fabian Keller, Emmanuel Müller, Klemens Böhm:
HiCS: High Contrast Subspaces for Density-Based Outlier Ranking. ICDE 2012: 1037-1048 - [c28]Emmanuel Müller, Stephan Günnemann, Ines Färber, Thomas Seidl:
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data. ICDE 2012: 1207-1210 - [c27]Emmanuel Müller, Ira Assent, Patricia Iglesias Sánchez, Yvonne Mülle, Klemens Böhm:
Outlier Ranking via Subspace Analysis in Multiple Views of the Data. ICDM 2012: 529-538 - [c26]Emmanuel Müller, Fabian Keller, Sebastian Blanc, Klemens Böhm:
OutRules: A Framework for Outlier Descriptions in Multiple Context Spaces. ECML/PKDD (2) 2012: 828-832 - [c25]Andranik Khachatryan, Emmanuel Müller, Christian Stier, Klemens Böhm:
Sensitivity of Self-tuning Histograms: Query Order Affecting Accuracy and Robustness. SSDBM 2012: 334-342 - [e2]Emmanuel Müller, Thomas Seidl, Suresh Venkatasubramanian, Arthur Zimek:
3rd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings, MultiClust '12, in conjunction with SDM 2012, Anaheim, CA, USA, April 28, 2012, Anaheim, CA, USA, April 28, 2012. SIAM 2012 [contents] - 2011
- [c24]Emmanuel Müller, Ira Assent, Stephan Günnemann, Patrick Gerwert, Matthias Hannen, Timm Jansen, Thomas Seidl:
A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases. BTW 2011: 347-366 - [c23]Emmanuel Müller, Ira Assent, Stephan Günnemann, Thomas Seidl:
Scalable density-based subspace clustering. CIKM 2011: 1077-1086 - [c22]Stephan Günnemann, Ines Färber, Emmanuel Müller, Ira Assent, Thomas Seidl:
External evaluation measures for subspace clustering. CIKM 2011: 1363-1372 - [c21]Emmanuel Müller, Matthias Schiffer, Thomas Seidl:
Statistical selection of relevant subspace projections for outlier ranking. ICDE 2011: 434-445 - [c20]Stephan Günnemann, Emmanuel Müller, Sebastian Raubach, Thomas Seidl:
Flexible Fault Tolerant Subspace Clustering for Data with Missing Values. ICDM 2011: 231-240 - [c19]