Patents
  • Ankerst M.:
    • Tight Integration of Processing and Visualizing Temporal Data
    • US Patent Application. 2004
  • Ankerst M., Kao.:
    • Large-Scale Visualization of Temporal Data
    • US Patent Application. 2004

Selected Publications
    [23] Keim D., Sips M., Ankerst M.,:
    • Visual Data Mining
    • Chapter in book Visualization Handbook
    • Eds. Johnson C.R., Hansen C.D., Academic Press, 2004
    [22] Ankerst M., Jones D.H., Kao A., Wang C.:
    • DataJewel: Tightly Integrating Visualization with Temporal Data Mining
    • ICDM Workshop on Visual Data Mining, Melbourne, FL, 2003
    [21] Ankerst M., Jones D.H., Kao A., Wang C.:
    • Temporal Data Mining of Airplane Maintenance Data
    • BTEC 5 Boeing Technical Excellence Conference, Seattle, WA, 2003
    [20] Wang C., Zhang W., Ankerst M., Kao A.:
    • Discovering Unexpected Temporal Patterns from Event Histories
    • BTEC 5 Boeing Technical Excellence Conference, Seattle, WA, 2003
    [19] Ankerst M., Keim D.A.:
    • Visual Data Mining
    • Tutorial at SIAM Int. Conf on Data Mining 2003, San Francisco, CA
    [18] Ankerst M.:
    • The perfect Data Mining Tool: Automated or Interactive?
    • Panelists: S. Chauduri, G. Grinstein, J. Han, G. Piatetski-Shapiro
    • Panel (chair) at ACM SIGKDD'2002, Edmonton, Canada. SIGKDD Explorations article
    [17] Grinstein G., Ankerst M., Keim D.A.:
    • Visual Data Mining: Background, Applications, and Drug Discovery Applications
    • Tutorial at ACM SIGKDD'2002, Edmonton, Canada.
    [16] Keim D.A., Ankerst M.: [15] Ankerst M.:
    • Visual Data Mining with Pixel-oriented Visualization Techniques
    • ACM SIGKDD Workshop on Visual Data Mining, San Francisco, CA, 2001 (pdf, 1MB)
    [14] Ankerst M.:
    • Human Involvement and Interactivity of the Next Generation's Data Mining Tools
    • ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Santa Barbara, CA, 2001
    [13] Ankerst M.:
    • Visual Data Mining
    • Dissertation (Ph.D. thesis), Faculty of Mathematics and Computer Science, University of Munich, 2000. Published by www.dissertation.de, ISBN: 3-89825-201-9
    [12] Ankerst M., Ester M., Kriegel H.-P.:
    • Towards an Effective Cooperation of the Computer and the User for Classification
    • ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining (KDD'2000), Boston, MA, 2000, Paper(pdf)
    [11] Ankerst M., Elsen C., Ester M., Kriegel H.-P.:
    • Perception-Based Classification
    • Informatica, An International Journal of Computing and Informatics, Vol. 23, No. 4, 1999
    [10] Ankerst M., Elsen C., Ester M., Kriegel H.-P.:
    • Visual Classification: An Interactive Approach to Decision Tree Construction
    • ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining (KDD'99), San Diego, CA, 1999 Paper(pdf)
    [09] Ankerst M., Kastenmueller, G., Kriegel H.-P., Seidl T.:
    • Nearest Neighbor Classification in 3D Protein databases
    • Proc. 7th Int. Conf. on Intelligent Systems for Molecular Biology (ISMB`99), Heidelberg, Germany, 1999
    [08] Ankerst M., Kastenmueller, G., Kriegel H.-P., Seidl T.:
    • 3D Shape Histograms for Similarity Search and Classification in Spatial Databases
    • Proc. 6th Int. Symposium on Large Spatial Databases (SSD`99), Hong Kong, China
    [07] Ankerst M., Breunig M., Kriegel H.-P., Sander J.:
    • OPTICS: Ordering Points To Identify the Clustering Structure
    • Proc. ACM SIGMOD Int. Conf. on Management of Data, Philadelphia, PA, 1999
    [06] Ankerst M., Kriegel H.-P., Seidl T.:
    • A Multi-Step Approach for Shape Similarity in Image Databases
    • IEEE TKDE special issue, Vol. 10, No. 6, 1998
    [05] Ankerst M., Berchtold S., Keim D. A.:
    • Similarity Clustering of Dimensions for an Enhanced Visualization of Multidimensional Data
    • Proc. Information Visualization, Phoenix, AZ, 1998
      • (zipped ps, 1.5MB)
    [04] Ankerst M., Braunmüller B., Kriegel H.-P., Seidl T.:
    • Improving Adaptable Similarity Query Processing by Using Approximations
    • Proc. 24th Int. Conf. on Very Large Data Bases, New York, 1998
    [03] Ankerst M.:
    • Circle Segments: Entwicklung und Evaluierung einer Visualisierungstechnik für Data Mining
    • Master Thesis (Diplomarbeit), Ludwig-Maximilians-Universität, München, 1997
    [02] Ankerst M., Keim D. A., Kriegel H.-P.:
    • 'Circle Segments': A Technique for Visually Exploring Large Multidimensional Data Sets
    • Proc. Visualization'96, Hot Topic Session, San Francisco, CA, 1996
    [01] Keim D. A., Kriegel H.-P., Ankerst M.:
    • Recursive Pattern: A Technique for Visualizing Very Large Amounts of Data
    • Proc. Visualization'95 Conf., Atlanta, GA, 1995, pp. 279-286

Program Commitee Member or Reviewer
  • ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining
  • VLDB Int. Conf. on Very Large Data Bases
  • SIAM Int. Conf. on Data Mining
  • IEEE Journal on Visualization and Computer Graphics
  • IEEE TVGC Trans. on Visualization and Computer Graphics
  • TKDE Journal on Trans. on Knowledge and Data Engineering
  • The Computer Journal
  • InfoVis, Symp. on Information Visualization