Technical Program for the First International Conference on Knowledge Discovery and Data Mining (KDD-95)

***************** Sunday - August 20, 1995 DAY 1 *********************

7:30 - 8:30 Registration

8:30 - 9:00 WELCOME, Opening remarks, Overview of KDD (U. Fayyad)

9:00 - 10:15 SESSION 1: Databases and Data Mining - Session Chair: Heikki Mannila

Applying a Data Miner To Heterogeneous Schema Integration:
Son Dao and Brad Perry, Hughes Research Laboratories

Active Data Mining:
Rakesh Agrawal and Giuseppe Psaila, IBM Almaden Research Center

A Database Interface for Clustering in Large Spatial Databases:
Martin Ester, Hans-Peter Kriegel, and Xiaowei Xu University of Munich, Germany

10:15 - 10:30 SPOTLIGHT SESSION 1 -- 6 posters (P1 through P6)

10:30 - 10:50 COFFEE BREAK

10:50 - 11:00 SPOTLIGHT SESSION 2 -- 4 posters (P7 through P10)

11:00 - 11:50 INVITED SPEAKER 1

David Haussler, UCSC:
Using Hidden Markov Models to Search Biosequence Databases

11:50 - 12:00 SPOTLIGHT SESSION 3 -- 4 posters (P11 through P14)

12:00 - 1:30 LUNCH BREAK
KDD95 Program Committee Lunch Meeting: La Cave Room, InterContinental Hotel

1:30 - 2:30 PANEL SESSSION

Commercial KDD Applications: The Secret Ingredients for Success
Panel Chairs: Gregory Piatetsky-Shapiro, GTE Laboratories and
Evangelos Simoudis, IBM Almaden Research Center

2:30 - 3:20 SESSION 2: Causality and Bayes Networks - Session Chair: Alex Tuzhilin

Available Technology for Discovering Causal Models, Building Bayes Nets, and Selecting Predictors: The TETRAD II Program:
Clark Glymour, Carnegie Mellon University

Learning Bayesian Networks with Discrete Variables from Data:
Peter Spirtes and Christopher Meek, Carnegie Mellon University

3:20 - 3:30 SPOTLIGHT SESSION 4 -- 3 posters (P15 through P17)

3:30 - 3:50 COFFEE BREAK

3:50 - 5:50 PARALLEL SESSION 3A: Rough Sets and Databases - Session Chair, Jan Zytkow

3:50 - 5:50 PARALLEL SESSION 3B: Supervised Learning - Session Chair: Willi Kloesgen

6:00 - 8:00 KDD-95 RECEPTION

6:00 - 8:00 POSTER SESSION 1

6:00 - 8:00 DEMO SESSION: Demo Session Chair: Tej Anand, AT&T Global Info. Solutions

***************** MONDAY - August 21, 1995 DAY 2 *********************

7:30 - 8:30 Registration

8:30 - 8:40 SPOTLIGHT SESSION 5 -- 4 posters (P18 through P21)

8:40 - 9:30 SESSION 4: Temporal Databases - Session Chair: Wray Buntine

Fast Spatio-Temporal Data Mining of Large Geophysical Datasets:
Paul Stolorz, JPL, et al.

Discovering Frequent Episodes in Sequences:
H. Mannila, H. Toivonen, and A.I. Verkamo, Univ. of Helsinki

9:30 - 9:40 SPOTLIGHT SESSION 6 -- 4 posters (P22 through P25)

9:40 - 10:30 INVITED SPEAKER 2

Tomasz Imielinski, Rutgers University:
A Database perspective on knowledge discovery

10:30 - 10:50 COFFEE BREAK

10:50 - 11:00 SPOTLIGHT SESSION 7 -- 4 posters (P26 through P29)

11:00 - 11:50 SESSION 5: Inductive Learning - Session Chair: Xindong Wu

MDL-Based Decision Tree Pruning:
M. Mehta, J. Rissanen, and R. Agrawal, IBM Almaden Res. Center

Estimating the Robustness of Discovered Knowledge:
Chun-Nan Hsu and Craig A. Knoblock, Univ. of Southern California

11:50 - 12:00 SPOTLIGHT SESSION 8 -- 4 posters (P30 through P33)

12:00 - 1:30 LUNCH BREAK

1:30 - 2:30 INVITED SPEAKER 3

Jerome Friedman, Stanford University:
Intelligent Local Learning: Statistical Algorithms for Prediction with High Dimensional Data

2:30 - 3:20 SESSION 6: KDD and STATISTICS - Session Chair: Padhraic Smyth

A Statistical Perspective On Knowledge Discovery In Databases:
John Elder, Rice Univ. and Daryl Pregibon, AT&T Bell Labs.

Discriminant Adaptive Nearest Neighbor Classification:
Trevor Hastie, Stanford University and Robert Tibshirani, University of Toronto

3:30 - 3:50 COFFEE BREAK

3:30 - 5:30 POSTER SESSION 2

3:30 - 5:30 DEMO SESSION Repeated
5:30 - 6:00 CONCLUDING REMARKS, SUMMARY and WRAP-UP Session (R. Uthurusamy)

PARALLEL SESSION 3A: Rough Sets and Databases
Sunday, August 20, 1995, 4:00 - 6:00pm

Discovery of Concurrent Data Models from Experimental Tables: A Rough Set Approach
Andrzej Skowron, Warsaw Univ. and Zbigniew Suraj, Pedagogical Univ., Poland

Automated Discovery of Functional Components of Proteins from Amino-Acid Sequences Based on Rough Sets and Change of Representation
Shusaku Tsumoto and Hiroshi Tanaka, Tokyo Medical and Dental Univ., Japan

Using Rough Sets as Tools for Knowledge Discovery
Ning Shan, Wojciech Ziarko, Howard J. Hamilton, and Nick Cercone, University of Regina, Canada

Exploiting Upper Approximation in the Rough Set Methodology
Jitender S. Deogun, University of Nebraska at Lincoln; Vijay V. Raghavan and Hayri Sever, University of Southwestern Louisiana

A Perspective on Databases and Data Mining
Marcel Holsheimer and Martin Kersten, CWI Database Res. Group, The Netherlands Heikki Mannila and Hannu Toivonen, University of Helsinki, Finland

Compression-Based Evaluation of Partial Determinations
Bernhard Pfahringer and Stefan Kramer, Austrian Research Inst. for AI, Austria

PARALLEL SESSION 3B: Supervised Learning: Issues and Applications
Sunday, August 20, 1995, 4:00 - 6:00pm

Knowledge Discovery in Telecommunication Services Data Using Bayesian Network Models:
Kazuo J. Ezawa and Steve W. Norton, AT&T Bell Laboratories

Analyzing the Benefits of Domain Knowledge in Substructure Discovery:
Surnjani Djoko, Diane J. Cook, and Lawrence B. Holder, University of Texas at Arlington

Decision Tree Induction: How Effective is the Greedy Heuristic?:
Sreerama K. Murthy and Steven Salzberg, Johns Hopkins University

Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology:
Ron Kohavi and Dan Sommerfield, Stanford University

Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning:
Philip K. Chan and Salvatore J. Stolfo, Columbia University

Are We Losing Accuracy While Gaining Confidence in Induced Rules: An Assessment of PrIL: F. Ozden Gur-Ali, GE Corporate Research and Development and William A. Wallace, Rensselaer Polytechnic Institute

DEMO SESSION :
Sunday, August 20, 1995, 6:00 - 8:00pm

Knowledge Discovery from Multiple Databases:
James Ribiero, George Mason University

Knowledge Discovery in Textual Databases:
Ronen Feldman, Bar-Ilan University

Exploiting Visualization in Knowledge Discovery
Hing-Yan Lee, Hwee-Leng Ong and Lee-Hian Quek, Information Technology Institute Singapore

KEFIR: The Key Findings Reporter for the analysis of healthcare information:
Christopher Matheus and Gregory Piatetsky-Shapiro, GTE Labs.

Automated Large-scale Data Mining by Forty-Niner (49er):
Arun Sanjeev and Jan Zytkow

POSTER SESSION 1:
Sunday, August 20, 1995, 6:00 - 8:00pm

SPOTLIGHT SESSION 1:

P1: STAR: A General Architecture for the Support of Distortion Oriented Displays:
Paul Anderson, Ray Smith, and Zhongwei Zhang, Monash University, Australia

P2: Learning First Order Logic Rules with a Genetic Algorithm:
S. Augier, G. Venturini, and Y. Kodratoff, Univ. Paris-Sud, France

P3: Discovery and Maintenance of Functional Dependencies by Independencies:
Siegfried Bell, University Dortmund, Germany

P4: Intelligent Instruments: Discovering How to Turn Spectral Data into Information:
Wray L. Buntine and Tarang Patel, NASA Ames Research Center

P5: Designing Neural Networks from Statistical Models: A New Approach to Data Exploration
Antonio Ciampi, McGill University, Canada and Yves Lechevallier INRIA-Rocquencourt, France

P6: Capacity and Complexity Control in Predicting the Spread Between Borrowing and Lending Interest Rates:
Corinna Cortes, Harris Drucker, Dennis Hoover, and Vladimir Vapnik, AT&T Bell Laboratories

SPOTLIGHT SESSION 2:

P7: Limits on Learning Machine Accuracy Imposed by Data Quality:
Corinna Cortes, L. D. Jackel, and Wan-Ping Chiang, AT&T Bell Laboratories

P8: Knowledge Discovery in a Water Quality Database:
Saso Dzeroski, Jozef Stefan Institute and Jasna Grbovic, Hydrometeorological Institute of Slovenia

P9: Data Mining for Loan Evaluation at ABN AMRO: A Case Study:
A. J. Feelders and A. J. F. le Loux, University of Twente; J. W. van't Zand, ABN AMRO Bank, The Netherlands

P10: Knowledge Discovery in Textual Databases (KDT):
Ronen Feldman and Ido Dagan, Bar-Ilan University, Israel

SPOTLIGHT SESSION 3:

P11: Optimization and Simplification of Hierarchical Clusterings:
Doug Fisher, Vanderbilt University

P12: Structured and Unstructured Induction with EDAGs:
Brian R. Gaines, University of Calgary, Canada

P13: Restructuring Databases for Knowledge Discovery by Consolidation and Link Formation:
Henry G. Goldberg and Ted E. Senator, Financial Crimes Enforcement Network (FinCEN), U.S. Dept. of Treasury

P14: Rough Sets Similarity-Based Learning from Databases:
Xiaohua Hu and Nick Cercone, University of Regina, Canada

SPOTLIGHT SESSION 4:

P15: Efficient Algorithms for Attribute-Oriented Induction:
Hoi-Yee Hwang and Wai-Chee Fu, Chinese University of Hong Kong

P16: Robust Decision Trees: Removing Outliers from Databases:
George H. John, Stanford University

P17: Conceptual Clustering in Structured Databases: A Practical Approach:
A. Ketterlin, P. Gancarski, and J. Korczak, LSIIT, Univ. Louis Pasteur, France

POSTER SESSION 2:
Monday, August 21, 1995, 3:30 - 5:30pm

SPOTLIGHT SESSION 5:

P18: Anonymization Techniques for Knowledge Discovery in Databases:
Willi Kloesgen, German National Research Center for Info. Technology (GMD)

P19: Exploiting Visualization in Knowledge Discovery:
Hing-Yan Lee, Hwee-Leng Ong, and Lee-Hian Quek, Information Technology Institute, Singapore

P20: Knowledge-Based Scientific Discovery in Geological Databases:
Cen Li and Gautam Biswas, Vanderbilt University

P21: An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers:
Michael J. Pazzani, University of California, Irvine

SPOTLIGHT SESSION 6:

P22: Knowledge Discovery from Multiple Databases:
James S. Ribeiro, Kenneth A. Kaufman, and Larry Kerschberg, George Mason University

P23: Discovering Enrollment Knowledge in University Databases:
Arun P. Sanjeev and Jan M. Zytkow, Wichita State University

P24: Extracting Support Data for a Given Task:
Bernhard Schoelkopf, Chris Burges, and Vladimir Vapnik, AT&T Bell Labs.

P25: Feature Extraction for Massive Data Mining:
V. Seshadri and Raguram Sasisekharan, AT&T Bell Laboratories; Sholom M. Weiss, Rutgers University

SPOTLIGHT SESSION 7:

P26: Data Surveying: Foundations of an Inductive Query Language:
Arno Siebes, CWI, Database Research Group, The Netherlands

P27: On Subjective Measures of Interestingness in Knowledge Discovery:
Avi Silberschatz, AT&T Bell Labs and Alexander Tuzhilin, New York Univ.

P28: Using Recon for Data Cleaning:
Evangelos Simoudis, IBM Almaden Research Center; Brian Livezey and Randy Kerber, Lockheed Palo Alto Research Laboratories

P29: Accelerated Quantification of Bayesian Networks with Incomplete Data:
Bo Thiesson, Aalborg University, Denmark

SPOTLIGHT SESSION 8:

P30: Automated Selection of Rule Induction Methods Based on Recursive Iteration of Resampling Methods and Multiple Statistical Testing:
Shusaku Tsumoto and Hiroshi Tanaka, Tokyo Medical and Dental Univ., Japan

P31: Fuzzy Interpretation of Induction Results:
Xindong Wu, Monash University, Australia and Petter Mahlen, Royal Institute of Technology, Sweden

P32: Resource and Knowledge Discovery in Global Information Systems: A Preliminary Design and Experiment:
Osmar R. Zaiane and Jiawei Han, Simon Fraser University, Canada

P33: Toward a Multi-Strategy and Cooperative Discovery System:
Ning Zhong, The Univ. of Tokyo and Setsuo Ohsuga, The Waseda Univ., Japan