The IJCAI-01 Workshop on
Learning from Temporal and Spatial Data

August 6th, 2001
Seattle, Washington, USA

In many application areas of machine learning and data mining, researchers face challenges entailed by temporal and spatial data.  This is the case of sales data analysis for targeted advertising, customer relationship management, fraud detection, intensive care monitoring, information filtering, user modelling, and learning from geographic or geo-referenced data.  As many of the related issues have already received due attention in the literature, time seems to be ripe for a one-day workshop to bring together a group of scientists specializing on this field.  The intention is to attempt to summarize the current status of the relevant work, and perhaps also identify strands for future research. 

The workshop will primarily focus on the following aspects: 

The tasks of interest include but are not limited to: 


Monday, August 6, 2001
  8:45- 9:00: Opening Remarks (Miroslav Kubat)

  9:00-  9:30:



Session 1

Katharina Morik (minitutorial):
       Summarizing Time Series and the Detection of Event Series (PDF)

Fu-lai Chung, Tak-chung Fu, Robert W. P. Luk, Vincent Ng:
       Flexible Time Series Pattern Matching Based on Perceptually Important Points

Malek Mouhoub:
       A Study of Numeric and Symbolic Time Information

10:30-11:00: Coffee Break




Session 2

Mark Maloof (minitutorial):
       On-line Learning With Partial Instance Memory (PDF.GZ, PS.GZ)

Ralf Klinkenberg:
       Using Labeled and Unlabeled Data to Learn Drifting Concepts

Frank Höppner:
       Learning Temporal Rules from State Sequences

12:30-14:00: Lunch Break




Session 3

Michael May (minitutorial):
       Spatial Data Mining
       (related research project: SPIN! -- Spatial Mining for Data of Public Interest)

Edwin P. D. Pednault, Chidanand Apte, Edna Grossman, Se June Hong:
       Insurance Risk Modeling and Customer Response Modeling Using
       Temporal and Spatial Data

Ursula Sondhauss, Claus Weihs:
       Incorporating Background Knowledge for Better Prediction of Cycle Phases

15:30-16:00: Coffee Break



Session 4

Ryszard Michalski (minitutorial):
       An Application of Symbolic Learning to Dynamic User Modeling
       and Pattern Discovery in Temporal Sequences (PDF)

Closing Remarks (Katharina Morik)


 Willi Klösgen 
 GMD - German National Research Center for Information Technology 
 AiS (Institute for Autonomous intelligent Systems) 
 Schloss Birlinghoven 
 53754 Sankt Augustin, Germany 
 Phone: +49 2241 14 2723 
 FAX: +49 2241 14 2072 

 Miroslav Kubat (Co-Chair) 
 University of Louisiana at Lafayette 
 Center for Advanced Computer Studies 
 P.O. Box 44330 
 Lafayette, LA 70504-4330, U.S.A. 
 Phone: (337) 482 6606 
 FAX: (337) 482 5791 

 Ryszard S. Michalski 
 George Mason University 
 Machine Learning and Inference Laboratory 
 4400 University Dr. 
 Fairfax, VA 22030, U.S.A. 
 Phone: (703) 993-1714 
 FAX: (703) 993-3729 or (703) 993-1710 

 Katharina Morik (Co-Chair) 
 Universität Dortmund 
 FB Informatik, LS8 
 Baroper Str. 301 
 44221 Dortmund, Germany 
 Phone: +49 231 755 5101 
 FAX: +49 231 755 5105 

URL of this page:
Last modified:   August 30, 2001 by Ralf Klinkenberg <>