Poster 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.

Session Chair (for the Poster Preview): Usama Fayyad, digiMine

• A Human-Computer Cooperative System for Effective High Dimensional Clustering
  Charu Aggarwal (IBM T. J. Watson Research Center)
  
  
• Mining Massively Incomplete Data Sets by Conceptual Reconstruction
  Charu Aggarwal (IBM T. J. Watson Research Center)
  Srinivasan Parthasarathy (Ohio State University)
  
  
• Data Mining Case Study: Modelling the Behaviour of Offenders Who Commit Serious Sexual Assaults
  Richard Adderley (West Midlands Police)
  Peter Musgrove (University of Wolverhampton)
  
  
• Solving Regression Problems with Rule-based Ensemble Classifiers
  Sholom Weiss (IBM Research)
  Nitin Indurkhya (IBM Research)
  
  
• Data filtering for automatic classification of rocks from reflectance spectra
  Jonathan Moody (Carnegie Mellon University)
  Ricardo Silva (Carnegie Mellon University)
  Joseph Vanderwaart (Carnegie Mellon University)
  Clark Glymour (Carnegie Mellon University and University of West Florida)
  
  
• Co-clustering documents and words using Bipartite Spectral Graph Parititioning
  Inderjit Dhillon (University of Texas)
  
  
• Gaining Insights into Support Vector Machine Pattern Classifiers Using Projection-Based Tour Methods
  Dianne Cook (Iowa State University)
  Doina Caragea (Iowa State University)
  Vasant Honavar (Iowa State University)
  
  
• A Robust and Scalable Clustering Algorithm for Mixed Type Attributes in Large Database Environment
  Tom Chiu (American Century Investments)
  DongPing Fang (SPSS Inc)
  John Chen (SPSS Inc)
  Yao Wang (SPSS Inc)
  Christopher Jeris (SPSS Inc)
  
  
• InfoMiner: Mining Surprising Periodic Patterns
  Jiong Yang (IBM TJ Watson)
  Wei Wang (IBM TJ Watson)
  Philip Yu (IBM T. J. Watson Research Center)
  
  
• A spectral method to separate disconnected and nearly-disconnected Web graph components
  Chris Ding (Lawrence Berkeley National Lab)
  Xiaofeng He (Lawrence Berkeley National Lab)
  
  
• Clustering Spatial Data Using Random Walks
  David Harel (The Weizmann Institute of Science)
  Yehuda Koren (The Weizmann Institute of Science)
  
  
• Impact rules: Finding associations with numeric variables
  Geoffrey Webb (Deakin University)
  
  
• Mining Frequent Neighboring Class Sets in Spatial Databases
  Yasuhiko Morimoto (IBM Tokyo Research Laboratory)
  
  
• Fast Ordering of Large Categorical Datasets for Better Visualization
  Alina Beygelzimer (Dept. of Computer Science, University of Rochester)
  Chang-Shing Perng (IBM T.J. Watson Research Center)
  Sheng Ma (IBM T.J. Watson)
  
  
• Discovering Outlier Filtering Rules from Unlabeled Data --Combining a Supervised Learner with an Unsupuervised Learner
  Kenji Yamanishi (NEC Corporation)
  Jun-ichi Takeuchi (NEC Corporation)
  
  
• PVA: A Self-Adaptive Personal View Agent System
  Chien Chin Chen (Institute of Information Science, Academia Sinica, Taiwan)
  Chen Meng Chang (nstitute of Information Science, Academia Sinica, Taiwan)
  Yeali Sun (Dept. of Information Management, National Taiwan University, Taiwan)
  
  
• Identifying Non-Actionable Association Rules
  Bing Liu (National U. of Singapore)
  Wynne Hsu (National U. of Singapore)
  Yiming Ma (National U. of Singapore)
  
  
• Discovering the Set of Fundamental Rule Changes
  Bing Liu (National U. of Singapore)
  Wynne Hsu (National U. of Singapore)
  Yiming Ma (National U. of Singapore)
  
  
• Finding simple intensity descriptions from event sequence data
  Heikki Mannila (Nokia Research Center)
  Marko Salmenkivi (University of Helsinki)
  
  
• TreeDT: Gene Mapping by Tree Disequilibrium Test
  Petteri Sevon (University of Helsinki)
  Hannu T.T. Toivonen (Nokia)
  Vesa Ollikainen (University of Helsinki)
  
  
• Random projection in dimensionality reduction: applications to image and text data
  Ella Bingham (Laboratory of Computer and Information Science)
  Heikki Mannila (Nokia Research Center)
  
  
• Mining Top-n Local Outliers in Very Large Databases
  Wen Jin (Simon Fraser University)
  Anthony K. H. Tung (Simon Fraser University)
  Jiawei Han (Simon Fraser University)
  
  
• Generalized Clustering, Supervised Learning, and Data Assignment
  Annaka Kalton (Stanford University)
  Pat Langley (Institute for the Study of Learning and Expertise)
  Kiri Wagstaff (Cornell University)
  Jungsoon Yoo (Middle Tennessee State University)
  
  
• The Distributed Boosting Algorithm
  Aleksandar Lazarevic (Temple University)
  Zoran Obradovic (Temple University)
  
  
• Mining A Stream of Transactions for Patterns in CustomerBehavior
  Diane Lambert (Bell Labs)
  Jose Pinheiro (Bell Labs)
  
  
• Evaluating the Novelty of Text-Mined Rules using Lexical Knowledge
  Sugato Basu (Dept. of Computer Sciences)
  Raymond J. Mooney (Dept. of Computer Sc., UT Austin)
  Krupakar V. Pasupuleti (Dept. of Computer Sc., UT Austin)
  Joydeep Ghosh (University of Texas, Austin)
  
  
• Experimental Comparisons of Online and Batch Versions of Bagging and Boosting
  Nikunj Oza (University of California, Berkeley)
  Stuart Russell (University of California, Berkeley)
  
  
• Detecting Graph-based Spatial Outliers: Algorithms and Applications
  Shashi Shekhar (University of Minnesota)
  Chang-Tien Lu (University of Minnesota)
  Pusheng Zhang (University of Minnesota)
  
  
• Concept Induction from Natural Language Text
  Dekang Lin (University of Alberta)
  Patrick Pantel (University of Alberta)
  
  
• DIRT: Discovery of Inference Rules from Text
  Dekang Lin (University of Alberta)
  Patrick Pantel (University of Alberta)
  
  
• Real World Performance of Association Rule Algorithms
  Zijian Zheng (Blue Martini Software)
  Ron Kohavi (Blue Martini Software)
  Llew Mason (Blue Martini Software)
  
  
• An Ensemble Method for Large-Scale Classification
  Nick Street (University of Iowa)
  YongSeog Kim (University of Iowa)