| A density-based algorithm for discovering clusters in large spatial databases with noise M Ester, HP Kriegel, J Sander, X Xu kdd 96 (34), 226-231, 1996 | 39905 | 1996 |
| LOF: identifying density-based local outliers MM Breunig, HP Kriegel, RT Ng, J Sander Proceedings of the 2000 ACM SIGMOD international conference on Management of …, 2000 | 12481 | 2000 |
| The R*-tree: An efficient and robust access method for points and rectangles N Beckmann, HP Kriegel, R Schneider, B Seeger Proceedings of the 1990 ACM SIGMOD international conference on Management of …, 1990 | 7440 | 1990 |
| OPTICS: Ordering points to identify the clustering structure M Ankerst, MM Breunig, HP Kriegel, J Sander ACM Sigmod record 28 (2), 49-60, 1999 | 7310 | 1999 |
| 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 | 3680 | 2017 |
| A three-way model for collective learning on multi-relational data. M Nickel, V Tresp, HP Kriegel Icml 11 (10.5555), 3104482-3104584, 2011 | 3378 | 2011 |
| The X-tree: An index structure for high-dimensional data S Berchtold, DA Keim, HP Kriegel | 2460 | 1996 |
| Density-based clustering in spatial databases: The algorithm gdbscan and its applications J Sander, M Ester, HP Kriegel, X Xu Data mining and knowledge discovery 2 (2), 169-194, 1998 | 2361 | 1998 |
| Integrating structured biological data by kernel maximum mean discrepancy KM Borgwardt, A Gretton, MJ Rasch, HP Kriegel, B Schölkopf, AJ Smola Bioinformatics 22 (14), e49-e57, 2006 | 2028 | 2006 |
| Protein function prediction via graph kernels KM Borgwardt, CS Ong, S Schönauer, SVN Vishwanathan, AJ Smola, ... Bioinformatics 21 (suppl_1), i47-i56, 2005 | 1637 | 2005 |
| Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering HP Kriegel, P Kröger, A Zimek Acm transactions on knowledge discovery from data (tkdd) 3 (1), 1-58, 2009 | 1562 | 2009 |
| Shortest-path kernels on graphs KM Borgwardt, HP Kriegel Fifth IEEE international conference on data mining (ICDM'05), 8 pp., 2005 | 1500 | 2005 |
| Density‐based clustering HP Kriegel, P Kröger, J Sander, A Zimek Wiley interdisciplinary reviews: data mining and knowledge discovery 1 (3 …, 2011 | 1405 | 2011 |
| Angle-based outlier detection in high-dimensional data HP Kriegel, M Schubert, A Zimek Proceedings of the 14th ACM SIGKDD international conference on Knowledge …, 2008 | 1284 | 2008 |
| 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 | 1213 | 2012 |
| Efficient processing of spatial joins using R-trees T Brinkhoff, HP Kriegel, B Seeger ACM SIGMOD Record 22 (2), 237-246, 1993 | 1042 | 1993 |
| 3D shape histograms for similarity search and classification in spatial databases M Ankerst, G Kastenmüller, HP Kriegel, T Seidl International symposium on spatial databases, 207-226, 1999 | 880 | 1999 |
| Incremental Clustering for Mining in a Data Warehousing Environment XX M Esrer,HP Kriegel,J Sander,M Wimmer Proc.24th VLDB Conference 24 (1998), 323-333, 1998 | 856* | 1998 |
| The pyramid-technique:towards breaking the curse of dimensionality HPK S Berchtold,C Böhm ACM SIGMOD Int.Conference on Management of Data 1998 (1998), 142-153, 1998 | 849 | 1998 |
| LoOP: local outlier probabilities HP Kriegel, P Kröger, E Schubert, A Zimek Proceedings of the 18th ACM conference on Information and knowledge …, 2009 | 774 | 2009 |
