Program
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  Papers
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KDD Cup
 
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Full Papers


The KDD-2001 program committee accepted 20 papers for full presentation, and an additional 32 papers for poster presentation, out of a total of 203 submission.

Best Paper

  • Robust Space Transformations for Distance-based Operations
    Edwin Knorr (Univ British Columbia)
    Raymond Ng (Univ British Columbia)
    Ruben Zamar (Univ British Columbia)

Session 1: Web Mining

Session Chair: Ronny Kohavi, Blue Martini

  • Discovering Unexpected Information from Your Competitors' Web Sites
    Bing Liu (National U. of Singapore)
    Yiming Ma (National U. of Singapore)
    Philip Yu (IBM T. J. Watson Research Center)

  • Personalization from Incomplete Data: What You Don't Know Can Hurt
    Balaji Padmanabhan (The Wharton School, Univ. of Pennsylvania)
    Zhiqiang Zheng (The Wharton School, University of Pennsylvania)
    Steven O. Kimbrough (The Wharton School, University of Pennsylvania)

  • Extracting collective probabilistic forecasts from Web games
    David M. Pennock (NEC Research Institute)
    Steve Lawrence (NEC Research Institute)
    C. Lee Giles (Pennsylvania State University)
    Finn Arup Nielsen (Technical University of Denmark)

Session 2: Applications

Session Chair: Heikki Mannila, Nokia

  • Molecular Feature Mining in HIV data
    Stefan Kramer (Albert-Ludwigs-University Freiburg)
    Luc De Raedt (Albert-Ludwigs-University Freiburg)
    Christoph Helma (Albert-Ludwigs-University Freiburg)

  • Empirical Bayes Screening For Multi-Item Associations In Massive Datasets
    William DuMouchel (AT&T Shannon Lab)
    Daryl Pregibon (AT&T Shannon Lab)

Session 3: Probabilistic Modeling

Session Chair: John Elder, Elder Research

  • Probabilistic Query Models for Transaction Data
    Dimitry Pavlov (UC Irvine)
    Padhraic Smyth (UC Irvine)

  • The ``DIGEX'' Distribution for Mining Massive, Skewed Data
    Zhiqiang Bi (Dept. of Physics, Carnegie Mellon University)
    Christos Faloutsos (Carnegie Mellon University)
    Flip Korn (AT&T Corp.)

  • Probabilistic Modeling of Transaction Data with Applications to Profiling, Visualization, and Prediction
    Igor Cadez (UC Irvine)
    Padhraic Smyth (UC Irvine)
    Heikki Mannila (Nokia Research Center)

  • Mining the Network Value of Customers
    Pedro Domingos (University of Washington)
    Matt Richardson (University of Washington)

Session 4: Visualization & Interpretability

Session Chair: Ramesh Natarajan, IBM T.J.Watson Research Center

  • Visualizing Multi-dimensional Clusters, Trends, and Outliers using Star Coordinates
    Eser Kandogan (IBM)

  • Tri-Plots: Scalable Tools for Multidimensional Data Mining
    Agma Traina (University of S. Paulo at S. Carlos - Brazil)
    Caetano Traina (Dept. of Computer Science and Statistics, University of S. Paulo at S. Carlos, Brazil)
    Spiros Papadimitriou (Computer Science Dept, Carnegie Mellon University)
    Christos Faloutsos (Dept. of Computer Science, Carnegie Mellon University, USA)

  • Data Mining Criteria for Tree-Based Regression and Classification
    Andreas Buja (AT&T Labs)
    Yung-Seop Lee (Dongguk University, Korea)

Session 5: Classification & Regression

Session Chair: Richard Caruana, Cornell University

  • Proximal Support Vector Machine Classifiers
    Glenn Fung (University of Wisconsin)
    Olvi Mangasarian (University of Wisconsin)

  • Learning and Making Decisions When Costs and Probabilities are Both Unknown
    Bianca Zadrozny (University of California, San Diego)
    Charles Elkan (CSE 0114)

  • Mining Time-Changing Data Streams
    Geoff Hulten (University of Washington)
    Laurie Spencer (SHAI)
    Pedro Domingos (University of Washington)

  • Data mining with sparse grids using simplicial basis functions
    Jochen Garcke (IAM, Universitaet Bonn)
    Michael Griebel (IAM, Universitaet Bonn)

Session 6: High Dimensional Data

Session Chair: Christos Faloutsos, Carnegie Mellon University

  • Using Ensembles of Representations for Indexing Large Databases
    Eamonn Keogh (UC Irvine)
    Selina Chu (ICS UCI)
    Michael Pazzani (ICS UCI)

  • Efficient Discovery of Error-Tolerant Frequent Itemsets in High Dimensions
    Cheng Yang (Stanford University)
    Usama Fayyad (digiMine, Inc)
    Paul Bradley (digiMine, Inc.)

  • GESS: a Scalable Similarity-Join Algorithm for Mining Large Data Sets in High Dimensional Spaces
    Jens-Peter Dittrich (University of Marburg)
    Bernhard Seeger (University of Marburg)