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Erich 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) - [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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