Alexander Hinneburg - Publications

• Alexander Hinneburg, Dirk Habich: Ähnlichkeitssuche in Musik-Datenbanken mit Hilfe von Visualisierungen (Kurzbeitrag), in Proceedings of 10. GI-Fachtagung Datenbanksysteme für Business, Technologie und Web, 2003, Leipzig, Germany.
• Alexander Hinneburg, Daniel A. Keim, Markus Wawryniuk: HD-Eye - Visual Clustering of High-Dimensional Data: A Demonstration, at 19th International Conference on Data Engineering, ICDE'03, 2003, Bangalore, India.
• Alexander Hinneburg, Matthias Fischer, Falk Bahner: Finding frequent substructures in 3D-protein databases, in Proc. of the Workshop on Bioinformatics in conjunction with ICDE 2003, Bangalore India.
• Alexander Hinneburg, Daniel A. Keim, Markus Wawryniuk: Using Projections to visually cluster high-dimensional Data, in IEEE Journal of Computing in Science and Engineering, Vol.5, No.2, March/April 2003, pp. 12-25.
• Alexander Hinneburg, Wolfgang Lehner: Database Support for 3D-Protein Data Set Analysis, in Proceedings of the 15th International Conference on Scientific and Statistical Database Management, 2003, Cambridge, Massachusetts, USA. IEEE Computer Society 2003, pp. 161-170.
• Alexander Hinneburg, Wolfgang Lehner, Dirk Habich: COMBI-Operator: Database Support for Data Mining, to appear in Proceedings of the 29th International Conference on Very Large Databases, VLDB 2003, Berlin, Germany.
• Alexander Hinneburg and Daniel A. Keim: A General Approach to Clustering in Large Databases with Noise in Journal of Knowledge and Information Systems (KAIS), Vol. 5, No. 4, pp. 387-415, Springer 2003.

• Alexander Hinneburg, Daniel A. Keim, Markus Wawryniuk:HD-Eye: Visual Clustering of High Dimensional Data. SIGMOD Conference 2002
• Daniel A. Keim, Martin Heczko, Alexander Hinneburg, Markus Wawryniuk: Multi-Resolution Similarity Search in Image Databases, in Proceedings of International Workshop on Multimedia Information Systems, 2002, Tempe, Arizona, USA, pp.76-85.

• Aggarwal C., Hinneburg A., Keim D.A.: On the Surprising Behavior of Distance Metrics in High Dimensional Space,in Proc. of 8th International Conference on Database Theory, ICDT 2001, London, pp. 420-434.PDF-Version (260kB)

• Hinneburg A., Keim D.A., and Brandt W.:Clustering 3D-structures of Small Aminoacid-chains for Detecting Dependence from Their Sequential Context in Proteins, in Proc. of IEEE International Symposium on Bio-Informatics & Biomedical Engineering (BIBE 2000),Washington DC, 2000, pp.43-49. PDF-Version (1.4MB)
• Hinneburg A., Keim D.A.:Werkzeuge zur interaktiven Clusteranalyse, in Proc. of the annual meeting of the GI Machine Learning Group (FGML 2000), GMD Report 114, Bonn, 2000. (Abstract in German) PDF-Version (100kB)
• Hinneburg A., Aggarwal C., Keim D.A.:What is the nearest neighbor in highdimensional spaces, in Proc. 26th Int. Conf. on Very Large Data Bases, Cairo, 2000. PDF-Version (300kB)
• Hinneburg A., Keim D. A.: Clustering Techniques for Large Data Sets: From the Past to the Future ,Tutorial, Proc. Int. Conf. on Principles and Practize in Knowledge Discovery (PKDD'00), Lyon, France, 2000.
• Hinneburg A.: High Dimensional Clustering on Large Data Sets, in Proc. of the EDBT 2000 PhD Workshop, 2000. PDF-Version (57kB)

• Hinneburg A., Keim D.A., Wawryniuk M.:HD-Eye: Visual Mining High-dimensional Data , in IEEE Computer Graphics and Applications, Sept/Oct. 1999, Vol. 19 (5), pp.22-31.
• Hinneburg A., Keim D. A.: Cluster Discovery Methods for Large Data Bases: From the Past to the Future , Tutorial, Proc. ACM SIGMOD Int. Conf. on Management of Data, Philadelphia, PA, 1999.
• Hinneburg A., Keim D. A.: Clustering Techniques for Large Data Sets: From the Past to the Future ,Tutorial, Proc. Int. Conf. on Knowledge Discovery in Databases (KDD'99), San Diego, CA, 1999.
• Hinneburg A., Keim D.A.:Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering , in Proc. 25th Int. Conf. on Very Large Data Bases, Edinburgh, 1999, pp.506-517. Postscript-Version (570kB)
• Hinneburg A., Keim D.A.: Using Visual Interaction to solve Complex Optimization Problems, in Proc of the Dagstuhl Seminar on Visualization 2000.

• Hinneburg A., Keim D.A.: An Efficient Approach to Clustering in Multimedia Databases with Noise, Proc. 4rd Int. Conf. on Knowledge Discovery and Data Mining, New York, AAAI Press, 1998. Postscript-Version (800k)