Typical data mining approaches look for patterns in a single relation of a database. For many applications, squeezing data from multiple relations into a single table requires much thought and effort and can lead to loss of information. An alternative for these applications is to use multi-relational data mining. Multi-relational data mining can analyze data from a multi-relation database directly, without the need to transfer the data into a single table first. Thus the relations mined can reside in a relational or deductive database. Using multi-relational data mining it is often also possible to take into account background knowledge, which often corresponds to views in the database. 

Present MRDM approaches consider all of the main data mining tasks, including association analysis, classification, clustering, learning probabilistic models and regression. The pattern languages used by single-table data mining approaches for these data mining tasks have been extended to the multiple-table case. Relational pattern languages now include relational association rules, relational classification rules, relational decision trees, and probabilistic relational models, among others. MRDM algorithms have been developed to mine for patterns expressed in relational pattern languages. Typically, data mining algorithms have been upgraded from the single-table case: for example, distance-based algorithms for prediction and clustering have been upgraded by defining distance measures between examples/instances represented in relational logic. 

MRDM methods have been successfully applied accross many application areas, ranging from the analysis of business data, through bioinformatics (including the analysis of complete genomes) and pharmacology (drug design) to Web mining (information extraction from text and Web sources). 

The aim of the workshop is to bring together researchers and practitioners of data mining interested in methods for finding patterns in expressive languages from complex / multi-relational / structured data and their applications. 

Why the topic is of interest?

On the other hand, there is a wealth of recent work concerned with upgrading some recent successful data mining approaches to relational logic. A case in point are kernel methods (support-vector machines): the development of kernels for structured and richer data types is a hot research topic. Another example is the development of probabilistic relational representations and methods for learning in them (e.g., probabilistic relational models, first-order Bayesian networks, stochastic logic programs, etc.) 

Non-exclusive list of topics. listed in alphabetical order: 

• Applications of (multi-)relational data mining 
• Data mining problems that require (multi-)relational methods 
• Distance-based methods for structured/relational data 
• Inductive databases 
• Kernel methods for structured/relational data 
• Learning in probabilistic relational representations 
• Link analysis and discovery 
• Methods for (multi-)relational data mining 
• Mining structured data, such as amino-acid sequences, chemical compounds, HTML and XML documents, 
• Propositionalization methods for transforming (multi-)relational data mining problems to single-table data mining problems 
• Relational neural networks 
• Relational pattern languages 

Contact information of organizers

• Saso Dzeroski (Saso.Dzeroski@ijs.si) (contact)

Jozef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia.
phone: +386 1 477 3217, fax: +386 1 425 1083 
• Luc De Raedt

Albert-Ludwigs-Universitaet Freiburg, Germany. 
• Stefan Wrobel

Otto-von-Guericke-Universitaet Magdeburg, Germany. 

Program Committee Members

 
• Hendrik Blockeel (Katholieke Universiteit Leuven) 
• Jean-Francois Boulicaut (University of Lyon) 
• Diane Cook (University of Texas at Arlington) 
• Mark Craven (University of Wisconsin at Madison) 
• Luc Dehaspe (PharmaDM) 
• Pedro Domingos (University of Washington) 
• Peter Flach (University of Bristol) 
• Lise Getoor (University of Maryland) 
• David Jensen (University of Massachusets at Amherst 
• Ross King (University of Aberystwith) 
• Stefan Kramer (Albert-Ludwigs-Universitaet Freiburg) 
• Nada Lavrac (Jozef Stefan Institute) 
• Donato Malerba (University of Bari) 
• Heikki Mannila (Nokia Research / Helsinki Institute for Information Technology) 
• Tom Mitchell (Carnegie Mellon University) 
• Hiroshi Motoda (University of Osaka) 
• Stephen Muggleton (Imperial College) 
• David Page (University of Wisconsin at Madison) 
• Foster Provost (Stern School of Business, New York University) 
• Celine Rouveirol (University Paris Sud XI) 
• Gunter Saake (Otto-von-Guericke Universitaet Magdeburg) 
• Michele Sebag (University Paris Sud XI) 
• Arno Siebes (Universiteit Utrecht) 
• Hannu Toivonen (University of Helsinki / Nokia Research) 

Important Dates

• Submissions due: May 27, 2002
• Notification: June 10, 2002
• Camera ready: June 24, 2002
• Workshop day: July 23, 2002

Accepted Papers

• Discovering Knowledge from Relational Data Extracted from Business News.
  A. Bernstein, S. Clearwater, S. Hill, C. Perlich and F. Provost.
  
  
• Hierarchical Multi-Classification.
  H. Blockeel, M. Bruynooghe, S. Dzeroski, J. Ramon, and J, Struyf.
  
  
• Statistical Models for Relational Data.
  L. Getoor, D. Koller, and B. Taskar.
  
  
• Schemas and Models.
  D. Jensen and J. Neville.
  
  
• Concept Formation Using Graph Grammars.
  I. Jonyer, L. B. Holder, and D. J. Cook.
  
  
• Constraint Based Mining of First-Order Sequences in SeqLog.
  S. D. Lee and L. De Raedt.
  
  
• Experiments With MRDTL -- A Multi-Relational Decision Tree Learning Algorithm.
  H. Leiva and V. Honavar.
  
  
• Mining Patterns from Structured Data by Beam-wise Graph-Based Induction.
  T. Matsuda, H. Motoda, T. Yoshida, and T. Washio.
  
  
• Structural Logistic Regression: Combining Relational and Statistical Learning.
  A. Popescul, L.H. Ungar, S. Lawrence, and D.M. Pennock.