Student Paper Presentations
     

    2000.02.18 (Fri)
  1. Tuan M.Nguyen:
    2. Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem
    Mauricio A. Hernández, Salvatore J. Stolfo
    Data Mining and Knowledge Discovery, 1997, pp. 9-37
  2. Thomas Soong:
    3. Chameleon: Hierarchical Clustering Using Dynamic Modeling
    George Karypis, Eui-Hong (Sam) Han, Vipin Kumar
    IEEE Computer, 32(8), 1999 Aug, pp. 68-75
  3. Asha Nallana:
    4. User Profiling in Personalization Applications through Rule Discovery and Validation
    Gediminas Adomavicius, Alexander Tuzhilin
    Proc. 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), Aug 1999, pp 377-381
  4. Anhdung Ngo:
    5. Sampling Large Databases for Association Rules
    Hannu Toivonen
    In 22th International Conference on Very Large Databases (VLDB'96), 134-145, Mumbay, India, September 1996. Morgan Kaufmann

    2000.02.19 (Sat)
  5. Brian Buras:
    6. Using Association Rules for Product Assortment Decisions: A Case Study
    Tom Brijs, Gilbert Swinnen, Koen Vanhoof, Geert Wets
    Proc. 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), Aug 1999, pp 254-260
  6. Margaret Millikin:
    7. A Statistical Theory for Quantitative Association Rules
    Yonatan Aumann, Yehuda Lindell
    Proc. 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), Aug 1999, pp 261-270
  7. Balaji Natrajan:
    8. SLIQ: A Fast Scalable Classifier for Data Mining
    Manish Mehta, Rakesh Agrawal and Jorma Rissanen
    Proc. Fifth Int'l Conference on Extending Database Technology, Avignon, France, Mar 1996

    2000.03.24 (Fri)
  8. Derek Saldana:
    9. Comprehensible Knowledge Discovery: Gaining Insight from Data
    Michael J. Pazzani,
    First Federal Data Mining Conference and Exposition, pp 73-82, Washington, DC.
  9. Harold Zhu:
    10. Support vector classifiers: a first look
    David M.J. Tax, D. de Ridder, Robert P.W. Duin
    Proceedings of the Third Annual Conference of the Advanced School for Computing and Imaging, ASCI, Delft, June 1997
  10. Charles Cao:
    11. On Support Vector Decision Trees for Database Marketing
    Kristin P. Bennett, D. H. Wu, L. Auslender
    .P.I Math Report No. 98-100, Rensselaer Polytechnic Institute, Troy, NY, 1998

    2000.03.25 (Sat)
  11. Bill Stump:
    12. Visualization Techniques for Mining Large Databases: A Comparison
    Daniel A. Keim, Hans-Peter Kriegel.
    IEEE Transactions on Knowledge and Data Engineering, Special Issue on Data Mining, Vol. 8, No. 6, 1996, pp 923-938
  12. Sanjeev Mohan:
    13. What Makes Patterns Interesting in Knowledge Discovery Systems
    Avi Silberschatz, Alexander Tuzhilin
    IEEE Transactions on Knowledge and Data Engineering Vol. 8, No. 6, Dec 1996, pp 970-974
  13. Michael Fahy:
    14. Density Biased Sampling: An Improved Method for Data Mining and Clustering 
    Christopher R. Palmer and Christos Faloutsos
    CMU Technical Report CMU-CS-99-113 

    2000.04.14 (Fri)
  14. Bob France:
    The Effects of Training Set Size on Decision Tree Complexity
    Tim Oates, David Jensen
    Proceedings of the 14th International Conference on Machine Learning. 1997
  15. David Vickers:
    16. Interactive Data Analysis: The Control Project
    Joseph M. Hellerstein, et al.
    The CONTROL project
    IEEE Computer, 32(8), Aug, 1999, pp. 51-59
  16. Morris E. Albers II:
    17. Statistics and Data Mining Techniques for Lifetime Value Modeling
    D.R. Mani, James Drew, Andrew Betz, Piew Datta
    Proc. 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), Aug 1999, pp 94-103

    2000.04.15 (Sat)
  17. Robert Goluba:
    18. Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
    Rakesh Agrawal, King-Ip Lin, Harpreet S. Sawhney, and Kyuseok Shim
    Proc. 21st International Conference on Very Large Databases, Zurich, Switzerland, Sep 1995
  18. David Alvarez:
    19. Searching the World Wide Web
    Steve Lawrance and C. Lee Giles Science 280, p. 98, April 3, 1998

    2000.05.13 (Sat)
  19. Howard Curtis:
    Empirical Analysis of Predictive Algorithms for Collaborative Filtering
    John S. Breese, David Heckerman, Carl Kadie
    Microsoft Research Technical Report MSR-TR-98-12, May 1998
  20. Giang C. Nguyen
    Mining the Web's Link Structure
    Soumen Chakrabarti, Byron E. Dom, S. Ravi Kumar, Prabhakar Raghav an , Sridhar Rajagopalan, Andrew Tomkins, David Gibson, and Jon Kleinberg
    IEEE Computer, 32(8), Aug, 1999, pp. 60-67