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ORCIDErich Schubert
Person information
- affiliation: Technical University of Dortmund, Germany
- affiliation (former): Universität Heidelberg, Germany
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2020 – today
- 2024
[j18]Lars Lenssen
, Erich Schubert:
Medoid Silhouette clustering with automatic cluster number selection. Inf. Syst. 120: 102290 (2024)- [c51]Miriama Jánosová, Andreas Lang, Petra Budíková, Erich Schubert, Vlastislav Dohnal:
Advancing the PAM Algorithm to Semi-supervised k-Medoids Clustering. SISAP 2024: 223-237 - [c50]Erich Schubert:
Hierarchical Clustering Without Pairwise Distances by Incremental Similarity Search. SISAP 2024: 238-252 - [c49]Erik Thordsen, Erich Schubert:
Grouping Sketches to Index High-Dimensional Data in a Resource-Limited Setting. SISAP 2024: 274-282 - [i19]Erik Thordsen, Erich Schubert:
Explicit Formulae to Interchangeably use Hyperplanes and Hyperballs using Inversive Geometry. CoRR abs/2405.18401 (2024) - 2023
- [j17]Erich Schubert:
Stop using the elbow criterion for k-means and how to choose the number of clusters instead. SIGKDD Explor. 25(1): 36-42 (2023) - [c48]Melanie Derksen, Julia Becker, Mohammad Fazleh Elahi, Angelika Maier, Marius Maile, Ingo Oliver Pätzold, Jonas Penningroth, Bettina Reglin, Markus Rothgänger, Philipp Cimiano, Erich Schubert, Silke Schwandt, Torsten W. Kuhlen, Mario Botsch, Tim Weissker:
Who Did What When? Discovering Complex Historical Interrelations in Immersive Virtual Reality. ISMAR 2023: 129-137 - [c47]Lars Lenssen, Niklas Strahmann, Erich Schubert:
Fast k-Nearest-Neighbor-Consistent Clustering. LWDA 2023: 387-398 - [c46]Erik Thordsen, Erich Schubert:
An Alternating Optimization Scheme for Binary Sketches for Cosine Similarity Search. SISAP 2023: 41-55 - [c45]Andreas Lang, Erich Schubert:
Accelerating k-Means Clustering with Cover Trees. SISAP 2023: 148-162 - [i18]Erich Schubert, Andreas Lang:
Data Aggregation for Hierarchical Clustering. CoRR abs/2309.02552 (2023) - [i17]Lars Lenssen, Erich Schubert:
Sparse Partitioning Around Medoids. CoRR abs/2309.02557 (2023) - [i16]Lars Lenssen, Erich Schubert:
Medoid Silhouette clustering with automatic cluster number selection. CoRR abs/2309.03751 (2023) - 2022
- [j16]Franka Bause, Erich Schubert, Nils M. Kriege:
EmbAssi: embedding assignment costs for similarity search in large graph databases. Data Min. Knowl. Discov. 36(5): 1728-1755 (2022) - [j15]Andreas Lang, Erich Schubert:
BETULA: Fast clustering of large data with improved BIRCH CF-Trees. Inf. Syst. 108: 101918 (2022) - [j14]Erik Thordsen, Erich Schubert:
ABID: Angle Based Intrinsic Dimensionality - Theory and analysis. Inf. Syst. 108: 101989 (2022) - [j13]Erich Schubert, Lars Lenssen:
Fast k-medoids Clustering in Rust and Python. J. Open Source Softw. 7(75): 4183 (2022) - [c44]Erik Thordsen, Erich Schubert:
On Projections to Linear Subspaces. SISAP 2022: 75-88 - [c43]Lars Lenssen, Erich Schubert:
Clustering by Direct Optimization of the Medoid Silhouette. SISAP 2022: 190-204 - [c42]Erich Schubert:
Automatic Indexing for Similarity Search in ELKI. SISAP 2022: 205-213 - [p2]Lars Lenssen, Erich Schubert:
Sparse Partitioning Around Medoids. Mach. Learn. under Resour. Constraints Vol. 1 (1) 2022: 182-196 - [p1]Erich Schubert, Andreas Lang:
Data Aggregation for Hierarchical Clustering. Mach. Learn. under Resour. Constraints Vol. 1 (1) 2022: 215-226 - [i15]Erik Thordsen, Erich Schubert:
On Projections to Linear Subspaces. CoRR abs/2209.12485 (2022) - [i14]Lars Lenssen, Erich Schubert:
Clustering by Direct Optimization of the Medoid Silhouette. CoRR abs/2209.12553 (2022) - [i13]Erich Schubert:
Stop using the elbow criterion for k-means and how to choose the number of clusters instead. CoRR abs/2212.12189 (2022) - [i12]Daniel Boiar, Thomas Liebig, Erich Schubert:
LOSDD: Leave-Out Support Vector Data Description for Outlier Detection. CoRR abs/2212.13626 (2022) - 2021
- [j12]Erich Schubert, Peter J. Rousseeuw:
Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms. Inf. Syst. 101: 101804 (2021) - [c41]Erich Schubert:
HACAM: Hierarchical Agglomerative Clustering Around Medoids - and its Limitations. LWDA 2021: 191-204 - [c40]Erik Thordsen, Erich Schubert:
CANDLE: Classification And Noise Detection With Local Embedding Approximations. LWDA 2021: 219-231 - [c39]Erich Schubert:
A Triangle Inequality for Cosine Similarity. SISAP 2021: 32-44 - [c38]Erich Schubert, Andreas Lang, Gloria Feher:
Accelerating Spherical k-Means. SISAP 2021: 217-231 - [c37]Erik Thordsen, Erich Schubert:
MESS: Manifold Embedding Motivated Super Sampling. SISAP 2021: 232-246 - [c36]Franka Bause, David B. Blumenthal, Erich Schubert, Nils M. Kriege:
Metric Indexing for Graph Similarity Search. SISAP 2021: 323-336 - [e4]Nora Reyes, Richard Connor, Nils M. Kriege, Daniyal Kazempour, Ilaria Bartolini, Erich Schubert, Jian-Jia Chen:
Similarity Search and Applications - 14th International Conference, SISAP 2021, Dortmund, Germany, September 29 - October 1, 2021, Proceedings. Lecture Notes in Computer Science 13058, Springer 2021, ISBN 978-3-030-89656-0 [contents] - [i11]Erich Schubert:
A Triangle Inequality for Cosine Similarity. CoRR abs/2107.04071 (2021) - [i10]Erich Schubert, Andreas Lang, Gloria Feher:
Accelerating Spherical k-Means. CoRR abs/2107.04074 (2021) - [i9]Erik Thordsen, Erich Schubert:
MESS: Manifold Embedding Motivated Super Sampling. CoRR abs/2107.06566 (2021) - [i8]Franka Bause, David B. Blumenthal, Erich Schubert, Nils M. Kriege:
Metric Indexing for Graph Similarity Search. CoRR abs/2110.01283 (2021) - [i7]Franka Bause, Erich Schubert, Nils M. Kriege:
EmbAssi: Embedding Assignment Costs for Similarity Search in Large Graph Databases. CoRR abs/2111.07761 (2021) - 2020
- [j11]Eirini Ntoutsi, Erich Schubert, Arthur Zimek, Albrecht Zimmermann:
Call for Special Issue Papers: Evaluation and Experimental Design in Data Mining and Machine Learning. Big Data 8(4): 253-254 (2020) - [j10]Eirini Ntoutsi, Erich Schubert, Arthur Zimek, Albrecht Zimmermann:
Call for Special Issue Papers: Evaluation and Experimental Design in Data Mining and Machine Learning. Big Data 8(5): 456-457 (2020) - [j9]Eirini Ntoutsi, Erich Schubert, Arthur Zimek, Albrecht Zimmermann:
Call for Special Issue Papers: Evaluation and Experimental Design in Data Mining and Machine Learning. Big Data 8(6): 546-547 (2020) - [c35]Erik Thordsen, Erich Schubert:
ABID: Angle Based Intrinsic Dimensionality. SISAP 2020: 218-232 - [c34]Andreas Lang, Erich Schubert:
BETULA: Numerically Stable CF-Trees for BIRCH Clustering. SISAP 2020: 281-296 - [e3]Irena Koprinska, Michael Kamp, Annalisa Appice, Corrado Loglisci, Luiza Antonie, Albrecht Zimmermann, Riccardo Guidotti, Özlem Özgöbek, Rita P. Ribeiro, Ricard Gavaldà, João Gama, Linara Adilova, Yamuna Krishnamurthy, Pedro M. Ferreira, Donato Malerba, Ibéria Medeiros, Michelangelo Ceci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras, Peter Christen, Eirini Ntoutsi, Erich Schubert, Arthur Zimek, Anna Monreale, Przemyslaw Biecek, Salvatore Rinzivillo, Benjamin Kille, Andreas Lommatzsch, Jon Atle Gulla:
ECML PKDD 2020 Workshops - Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): SoGood 2020, PDFL 2020, MLCS 2020, NFMCP 2020, DINA 2020, EDML 2020, XKDD 2020 and INRA 2020, Ghent, Belgium, September 14-18, 2020, Proceedings. Communications in Computer and Information Science 1323, Springer 2020, ISBN 978-3-030-65964-6 [contents] - [i6]Erik Thordsen, Erich Schubert:
ABID: Angle Based Intrinsic Dimensionality. CoRR abs/2006.12880 (2020) - [i5]Andreas Lang, Erich Schubert:
BETULA: Numerically Stable CF-Trees for BIRCH Clustering. CoRR abs/2006.12881 (2020) - [i4]Erich Schubert, Peter J. Rousseeuw:
Fast and Eager k-Medoids Clustering: O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithms. CoRR abs/2008.05171 (2020)
2010 – 2019
- 2019
- [j8]Laurent Amsaleg, Michael E. Houle, Erich Schubert:
Introduction to Special Issue of the 9th International Conference on Similarity Search and Applications (SISAP 2016). Inf. Syst. 80: 107 (2019) - [c33]Eirini Ntoutsi, Erich Schubert, Arthur Zimek, Albrecht Zimmermann:
1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2019). EDML@SDM 2019: 1-3 - [c32]Erich Schubert, Peter J. Rousseeuw:
Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms. SISAP 2019: 171-187 - [e2]Eirini Ntoutsi, Erich Schubert, Arthur Zimek, Albrecht Zimmermann:
Proceedings of the 1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning co-located with SIAM International Conference on Data Mining (SDM 2019), Calgary, Alberta, Canada, May 4th, 2019. CEUR Workshop Proceedings 2436, CEUR-WS.org 2019 [contents] - [i3]Erich Schubert, Arthur Zimek:
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg". CoRR abs/1902.03616 (2019) - 2018
- [c31]Erich Schubert, Andreas Spitz, Michael Gertz:
Exploring Significant Interactions in Live News. NewsIR@ECIR 2018: 39-44 - [c30]Erich Schubert, Michael Gertz:
Improving the Cluster Structure Extracted from OPTICS Plots. LWDA 2018: 318-329 - [c29]Erich Schubert, Sibylle Hess, Katharina Morik:
The Relationship of DBSCAN to Matrix Factorization and Spectral Clustering. LWDA 2018: 330-334 - [c28]Michael E. Houle, Erich Schubert, Arthur Zimek:
On the Correlation Between Local Intrinsic Dimensionality and Outlierness. SISAP 2018: 177-191 - [c27]Erich Schubert, Michael Gertz:
Numerically stable parallel computation of (co-)variance. SSDBM 2018: 10:1-10:12 - [r1]Arthur Zimek, Erich Schubert:
Outlier Detection. Encyclopedia of Database Systems (2nd ed.) 2018 - [i2]Erich Schubert, Peter J. Rousseeuw:
Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms. CoRR abs/1810.05691 (2018) - 2017
- [j7]Hans-Peter Kriegel, Erich Schubert, Arthur Zimek:
The (black) art of runtime evaluation: Are we comparing algorithms or implementations? Knowl. Inf. Syst. 52(2): 341-378 (2017) - [j6]Guillaume Casanova, Elias Englmeier, Michael E. Houle, Peer Kröger, Michael Nett, Erich Schubert, Arthur Zimek:
Dimensional Testing for Reverse k-Nearest Neighbor Search. Proc. VLDB Endow. 10(7): 769-780 (2017) - [j5]Erich Schubert, Jörg Sander, Martin Ester, Hans-Peter Kriegel, Xiaowei Xu:
DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN. ACM Trans. Database Syst. 42(3): 19:1-19:21 (2017) - [c26]Evelyn Kirner, Erich Schubert, Arthur Zimek:
Good and Bad Neighborhood Approximations for Outlier Detection Ensembles. SISAP 2017: 173-187 - [c25]Erich Schubert, Michael Gertz:
Intrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection - A Remedy Against the Curse of Dimensionality? SISAP 2017: 188-203 - [i1]Erich Schubert, Andreas Spitz, Michael Weiler, Johanna Geiß, Michael Gertz:
Semantic Word Clouds with Background Corpus Normalization and t-distributed Stochastic Neighbor Embedding. CoRR abs/1708.03569 (2017) - 2016
- [j4]Guilherme Oliveira Campos, Arthur Zimek, Jörg Sander, Ricardo J. G. B. Campello, Barbora Micenková, Erich Schubert, Ira Assent, Michael E. Houle:
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Min. Knowl. Discov. 30(4): 891-927 (2016) - [c24]Erich Schubert, Michael Weiler, Hans-Peter Kriegel:
SPOTHOT: Scalable Detection of Geo-spatial Events in Large Textual Streams. SSDBM 2016: 8:1-8:12 - [e1]Laurent Amsaleg, Michael E. Houle, Erich Schubert:
Similarity Search and Applications - 9th International Conference, SISAP 2016, Tokyo, Japan, October 24-26, 2016. Proceedings. Lecture Notes in Computer Science 9939, 2016, ISBN 978-3-319-46758-0 [contents] - 2015
- [j3]Erich Schubert, Alexander Koos, Tobias Emrich, Andreas Züfle, Klaus Arthur Schmid, Arthur Zimek:
A Framework for Clustering Uncertain Data. Proc. VLDB Endow. 8(12): 1976-1979 (2015) - [c23]Erich Schubert, Arthur Zimek, Hans-Peter Kriegel:
Fast and Scalable Outlier Detection with Approximate Nearest Neighbor Ensembles. DASFAA (2) 2015: 19-36 - [c22]Erich Schubert, Michael Weiler, Arthur Zimek:
Outlier Detection and Trend Detection: Two Sides of the Same Coin. ICDM Workshops 2015: 40-46 - 2014
- [j2]Erich Schubert, Arthur Zimek, Hans-Peter Kriegel:
Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection. Data Min. Knowl. Discov. 28(1): 190-237 (2014) - [c21]Xuan-Hong Dang, Ira Assent, Raymond T. Ng, Arthur Zimek, Erich Schubert:
Discriminative features for identifying and interpreting outliers. ICDE 2014: 88-99 - [c20]Erich Schubert, Michael Weiler, Hans-Peter Kriegel:
SigniTrend: scalable detection of emerging topics in textual streams by hashed significance thresholds. KDD 2014: 871-880 - [c19]Erich Schubert, Arthur Zimek, Hans-Peter Kriegel:
Generalized Outlier Detection with Flexible Kernel Density Estimates. SDM 2014: 542-550 - 2013
- [b1]Erich Schubert:
Generalized and efficient outlier detection for spatial, temporal, and high-dimensional data mining. Ludwig Maximilians University Munich, 2013, pp. 1-262 - [c18]Elke Achtert, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek:
Interactive data mining with 3D-parallel-coordinate-trees. SIGMOD Conference 2013: 1009-1012 - [c17]Erich Schubert, Arthur Zimek, Hans-Peter Kriegel:
Geodetic Distance Queries on R-Trees for Indexing Geographic Data. SSTD 2013: 146-164 - 2012
- [j1]Arthur Zimek, Erich Schubert, Hans-Peter Kriegel:
A survey on unsupervised outlier detection in high-dimensional numerical data. Stat. Anal. Data Min. 5(5): 363-387 (2012) - [c16]Elke Achtert, Sascha Goldhofer, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek:
Evaluation of Clusterings - Metrics and Visual Support. ICDE 2012: 1285-1288 - [c15]Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek:
Outlier Detection in Arbitrarily Oriented Subspaces. ICDM 2012: 379-388 - [c14]Erich Schubert, Remigius Wojdanowski, Arthur Zimek, Hans-Peter Kriegel:
On Evaluation of Outlier Rankings and Outlier Scores. SDM 2012: 1047-1058 - 2011
- [c13]Hans-Peter Kriegel, Erich Schubert, Arthur Zimek:
Evaluation of Multiple Clustering Solutions. MultiClust@ECML/PKDD 2011: 55-66 - [c12]Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek:
Interpreting and Unifying Outlier Scores. SDM 2011: 13-24 - [c11]Thomas Bernecker, Michael E. Houle, Hans-Peter Kriegel, Peer Kröger, Matthias Renz, Erich Schubert, Arthur Zimek:
Quality of Similarity Rankings in Time Series. SSTD 2011: 422-440 - [c10]Elke Achtert, Ahmed Hettab, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek:
Spatial Outlier Detection: Data, Algorithms, Visualizations. SSTD 2011: 512-516 - 2010
- [c9]Elke Achtert, Hans-Peter Kriegel, Lisa Reichert, Erich Schubert, Remigius Wojdanowski, Arthur Zimek:
Visual Evaluation of Outlier Detection Models. DASFAA (2) 2010: 396-399 - [c8]Thomas Bernecker, Tobias Emrich, Franz Graf, Hans-Peter Kriegel, Peer Kröger, Matthias Renz, Erich Schubert, Arthur Zimek:
Subspace similarity search using the ideas of ranking and top-k retrieval. ICDE Workshops 2010: 4-9 - [c7]Michael E. Houle, Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek:
Can Shared-Neighbor Distances Defeat the Curse of Dimensionality? SSDBM 2010: 482-500 - [c6]Thomas Bernecker, Tobias Emrich, Franz Graf, Hans-Peter Kriegel, Peer Kröger, Matthias Renz, Erich Schubert, Arthur Zimek:
Subspace Similarity Search: Efficient k-NN Queries in Arbitrary Subspaces. SSDBM 2010: 555-564
2000 – 2009
- 2009
- [c5]Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek:
LoOP: local outlier probabilities. CIKM 2009: 1649-1652 - [c4]Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek:
Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data. PAKDD 2009: 831-838 - [c3]Elke Achtert, Thomas Bernecker, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek:
ELKI in Time: ELKI 0.2 for the Performance Evaluation of Distance Measures for Time Series. SSTD 2009: 436-440 - 2008
- [c2]Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek:
A General Framework for Increasing the Robustness of PCA-Based Correlation Clustering Algorithms. SSDBM 2008: 418-435 - 2005
- [c1]Erich Schubert, Sebastian Schaffert, François Bry:
Structure-Preserving Difference Search for XML Documents. Extreme Markup Languages® 2005
Coauthor Index
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