| |
 |      |
                                         |
Note: Links lead to the DBLP on the Web. Alexander Hinneburg 14 Alexander Hinneburg, Dirk Habich : Ähnlichkeitssuche in Musik-Datenbanken mit Hilfe von Visualisierungen. BTW 2003 : 207-216 13 Alexander Hinneburg, Daniel A. Keim : Visual interaction to solving complex optimization problems. Data Visualization: The State of the Art 2003 : 407-422 12 Alexander Hinneburg, Wolfgang Lehner , Dirk Habich : COMBI-Operator: Database Support for Data Mining Applications. VLDB 2003 11 Daniel A. Keim , Martin Heczko , Alexander Hinneburg, Markus Wawryniuk : Multi-Resolution Similarity Search in Image Databases. Multimedia Information Systems 2002 : 76-85 10 Alexander Hinneburg, Daniel A. Keim , Markus Wawryniuk : HD-Eye: visual clustering of high dimensional data. SIGMOD Conference 2002 : 629 9 Charu C. Aggarwal , Alexander Hinneburg, Daniel A. Keim : On the Surprising Behavior of Distance Metrics in High Dimensional Spaces. ICDT 2001 : 420-434 8 Alexander Hinneburg, Daniel A. Keim , Wolfgang Brandt : Clustering 3D-structures of Small Aminoacid-chains for Detecting Dependence from Their Sequential Context in Proteins. BIBE 2000 : 43-49 7 Alexander Hinneburg: High Dimensional Clustering on Large Data Sets. EDBT PhD Workshop 2000 6 Alexander Hinneburg, Charu C. Aggarwal , Daniel A. Keim : What Is the Nearest Neighbor in High Dimensional Spaces? VLDB 2000 : 506-515 5 Daniel A. Keim , Alexander Hinneburg: Interesting KDD-News from SIGMOD'99. SIGKDD Explorations 1 (2): 117 (2000) 4 Daniel A. Keim , Alexander Hinneburg: Clustering Techniques for Large Data Sets - from the Past to the Future. KDD Tutorial Notes 1999 : 141-181 3 Alexander Hinneburg, Daniel A. Keim : Clustering Methods for Large Databases: From the Past to the Future. SIGMOD Conference 1999 : 509 2 Alexander Hinneburg, Daniel A. Keim : Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering. VLDB 1999 : 506-517 1 Alexander Hinneburg, Daniel A. Keim : An Efficient Approach to Clustering in Large Multimedia Databases with Noise. KDD 1998 : 58-65  DiSC'03 © 2003 Association for Computing Machinery |
