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Note: Links lead to the DBLP on the Web. Jörg Sander Samer Nassar , Jörg Sander, Corrine Cheng : Incremental and Effective Data Summarization for Dynamic Hierarchical Clustering. SIGMOD Conference 2004 Alexandru Coman , Jörg Sander, Mario A. Nascimento : Efficient Indexing of High Dimensional Normalized Histograms. DEXA 2003 : 601-610 Jörg Sander, Xuejie Qin , Zhiyong Lu , Nan Niu , Alex Kovarsky : Automatic Extraction of Clusters from Hierarchical Clustering Representations. PAKDD 2003 : 75-87 Jianjun Zhou , Jörg Sander: Data Bubbles for Non-Vector Data: Speeding-up Hierarchical Clustering in Arbitrary Metric Spaces. VLDB 2003 : 452-463 Mario A. Nascimento , Jörg Sander, Jeffrey Pound : Analysis of SIGMOD's co-authorship graph. SIGMOD Record 32 (3): 8-10 (2003) Raymond T. Ng , Jörg Sander, Monica C. Sleumer : Hierarchical cluster analysis of SAGE data for cancer profiling. BIOKDD 2001 : 65-72 Markus M. Breunig , Hans-Peter Kriegel , Peer Kröger , Jörg Sander: Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering. SIGMOD Conference 2001 Bernhard Braunmüller , Martin Ester , Hans-Peter Kriegel , Jörg Sander: Multiple Similarity Queries: A Basic DBMS Operation for Mining in Metric Databases. IEEE Trans. Knowl. Data Eng. 13 (1): 79-95 (2001) Bernhard Braunmüller , Martin Ester , Hans-Peter Kriegel , Jörg Sander: Efficiently Supporting Multiple Similarity Queries for Mining in Metric Databases. ICDE 2000 : 256-267 Stefan Berchtold , Christian Böhm , H. V. Jagadish , Hans-Peter Kriegel , Jörg Sander: Independent Quantization: An Index Compression Technique for High-Dimensional Data Spaces. ICDE 2000 : 577-588 Markus M. Breunig , Hans-Peter Kriegel , Jörg Sander: Fast Hierarchical Clustering Based on Compressed Data and OPTICS. PKDD 2000 : 232-242 Markus M. Breunig , Hans-Peter Kriegel , Raymond T. Ng , Jörg Sander: LOF: Identifying Density-Based Local Outliers. SIGMOD Conference 2000 : 93-104 Martin Ester , Alexander Frommelt , Hans-Peter Kriegel , Jörg Sander: Spatial Data Mining: Database Primitives, Algorithms and Efficient DBMS Support. Data Min. Knowl. Discov. 4 (2/3): 193-216 (2000) Martin Ester , Stefan Grundlach , Hans-Peter Kriegel , Jörg Sander: Database Primitives for Spatial Data Mining. BTW 1999 : 137-150 Martin Ester , Hans-Peter Kriegel , Jörg Sander: Knowledge Discovery in Spatial Databases. DAGM-Symposium 1999 : 1-14 Martin Ester , Hans-Peter Kriegel , Jörg Sander: Knowledge Discovery in Spatial Databases. KI 1999 : 61-74 Markus M. Breunig , Hans-Peter Kriegel , Raymond T. Ng , Jörg Sander: OPTICS-OF: Identifying Local Outliers. PKDD 1999 : 262-270 Mihael Ankerst , Markus M. Breunig , Hans-Peter Kriegel , Jörg Sander: OPTICS: Ordering Points To Identify the Clustering Structure. SIGMOD Conference 1999 : 49-60 Jörg Sander: Generalized Density-Based Clustering for Spatial Data Mining (Abstract). Datenbank Rundbrief 24 : 73 (1999) Xiaowei Xu , Martin Ester , Hans-Peter Kriegel , Jörg Sander: A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases. ICDE 1998 : 324-331 Martin Ester , Alexander Frommelt , Hans-Peter Kriegel , Jörg Sander: Algorithms for Characterization and Trend Detection in Spatial Databases. KDD 1998 : 44-50 Martin Ester , Hans-Peter Kriegel , Jörg Sander, Michael Wimmer , Xiaowei Xu : Incremental Clustering for Mining in a Data Warehousing Environment. VLDB 1998 : 323-333 Jörg Sander, Martin Ester , Hans-Peter Kriegel , Xiaowei Xu : Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications. Data Min. Knowl. Discov. 2 (2): 169-194 (1998) Martin Ester , Hans-Peter Kriegel , Jörg Sander, Xiaowei Xu : Clustering for Mining in Large Spatial Databases. KI 12 (1): 18-24 (1998) Martin Ester , Hans-Peter Kriegel , Jörg Sander, Xiaowei Xu : Density-Connected Sets and their Application for Trend Detection in Spatial Databases. KDD 1997 : 10-15 Martin Ester , Hans-Peter Kriegel , Jörg Sander: Spatial Data Mining: A Database Approach. SSD 1997 : 47-66 Martin Ester , Hans-Peter Kriegel , Jörg Sander, Xiaowei Xu : A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. KDD 1996 : 226-231 1 [ 10 ] 2 [ 18 ] 3 [ 18 ] 4 [ 19 ] [ 20 ] 5 [ 10 ] [ 11 ] [ 16 ] [ 17 ] [ 21 ] 6 [ 27 ] 7 [ 26 ] 8 [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ] [ 12 ] [ 13 ] [ 14 ] [ 15 ] [ 19 ] [ 20 ] 9 [ 7 ] [ 15 ] 10 [ 14 ] 11 [ 18 ] 12 [ 25 ] 13 [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ] [ 10 ] [ 11 ] [ 12 ] [ 13 ] [ 14 ] [ 15 ] [ 16 ] [ 17 ] [ 18 ] [ 19 ] [ 20 ] [ 21 ] 14 [ 21 ] 15 [ 25 ] 16 [ 23 ] [ 26 ] 17 [ 27 ] 18 [ 11 ] [ 16 ] [ 22 ] 19 [ 25 ] 20 [ 23 ] 21 [ 25 ] 22 [ 22 ] 23 [ 6 ] 24 [ 1 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 8 ] 25 [ 24 ]  ©2004 Association for Computing Machinery |
