| DBSCAN revisited, revisited: why and how you should (still) use DBSCAN E Schubert, J Sander, M Ester, HP Kriegel, X Xu ACM Transactions on Database Systems (TODS) 42 (3), 1-21, 2017 | 2746 | 2017 |
| A survey on unsupervised outlier detection in high‐dimensional numerical data A Zimek, E Schubert, HP Kriegel Statistical Analysis and Data Mining: The ASA Data Science Journal 5 (5 …, 2012 | 1068 | 2012 |
| On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study GO Campos, A Zimek, J Sander, RJGB Campello, B Micenková, ... Data mining and knowledge discovery 30, 891-927, 2016 | 868 | 2016 |
| LoOP: local outlier probabilities HP Kriegel, P Kröger, E Schubert, A Zimek Proceedings of the 18th ACM conference on Information and knowledge …, 2009 | 717 | 2009 |
| Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms E Schubert, PJ Rousseeuw Similarity Search and Applications: 12th International Conference, SISAP …, 2019 | 451 | 2019 |
| Outlier detection in axis-parallel subspaces of high dimensional data HP Kriegel, P Kröger, E Schubert, A Zimek Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia …, 2009 | 426 | 2009 |
| Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection E Schubert, A Zimek, HP Kriegel Data mining and knowledge discovery 28, 190-237, 2014 | 389 | 2014 |
| Interpreting and Unifying Outlier Scores HP Kriegel, P Kröger, E Schubert, A Zimek SIAM International Conference on Data Mining (SDM), 2011 | 375 | 2011 |
| Can shared-neighbor distances defeat the curse of dimensionality? ME Houle, HP Kriegel, P Kröger, E Schubert, A Zimek Scientific and Statistical Database Management: 22nd International …, 2010 | 374 | 2010 |
| The (black) art of runtime evaluation: Are we comparing algorithms or implementations? HP Kriegel, E Schubert, A Zimek Knowledge and Information Systems 52, 341-378, 2017 | 293 | 2017 |
| On Evaluation of Outlier Rankings and Outlier Scores E Schubert, R Wojdanowski, HP Kriegel, A Zimek SIAM Data Mining 2012, 2012 | 220 | 2012 |
| A framework for clustering uncertain data E Schubert, A Koos, T Emrich, A Züfle, KA Schmid, A Zimek Proceedings of the VLDB Endowment 8 (12), 1976-1979, 2015 | 218 | 2015 |
| Generalized outlier detection with flexible kernel density estimates E Schubert, A Zimek, HP Kriegel Proceedings of the 2014 SIAM international conference on data mining, 542-550, 2014 | 216 | 2014 |
| Fast and eager k-medoids clustering: O (k) runtime improvement of the PAM, CLARA, and CLARANS algorithms E Schubert, PJ Rousseeuw Information Systems 101, 101804, 2021 | 182 | 2021 |
| On using class-labels in evaluation of clusterings I Färber, S Günnemann, HP Kriegel, P Kröger, E Müller, E Schubert, ... MultiClust: 1st international workshop on discovering, summarizing and using …, 2010 | 173 | 2010 |
| Outlier Detection in Arbitrarily Oriented Subspaces HP Kriegel, P Kröger, E Schubert, A Zimek Data Mining (ICDM), 2012 IEEE 12th International Conference on, 379-388, 2012 | 159 | 2012 |
| A General Framework for Increasing the Robustness of PCA-Based Correlation Clustering Algorithms HP Kriegel, P Kröger, E Schubert, A Zimek Scientific and Statistical Database Management, 418-435, 2008 | 155 | 2008 |
| Evaluation of Clusterings–Metrics and Visual Support E Achtert, S Goldhofer, HP Kriegel, E Schubert, A Zimek IEEE 28th International Conference on Data Engineering (ICDE), 2012, 1285-1288, 2012 | 127 | 2012 |
| Signitrend: scalable detection of emerging topics in textual streams by hashed significance thresholds E Schubert, M Weiler, HP Kriegel Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 119 | 2014 |
| Discriminative features for identifying and interpreting outliers XH Dang, I Assent, RT Ng, A Zimek, E Schubert 2014 IEEE 30th international conference on data engineering, 88-99, 2014 | 117 | 2014 |
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