CFP

MRDM 2002 - Workshop on Multi-Relational Data Mining

organised at the

8th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining July 23 - 26, 2002, Edmonton, Alberta, Canada

Multi-Relational Data Mining (MRDM) is the multi-disciplinary field dealing with knowledge discovery from relational databases consisting of multiple tables. Mining data which consists of complex/structured objects also falls within the scope of this field, since the normalized representation of such objects in a relational database requires multiple tables. The field aims at integrating results from existing fields such as inductive logic programming, KDD, machine learning and relational databases; producing new techniques for mining multi-relational data; and practical applications of such tecniques.

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.

Paper submissions due: 21 May 2002.

Workshop Website: http://www-ai.ijs.si/SasoDzeroski/MRDM2002/ Workshop Contact: Saso Dzeroski (Saso.Dzeroski@ijs.si) Workshop Date: 23 July, 2002

Workshop chairs: Saso Dzeroski (Saso.Dzeroski@ijs.si), Luc De Raedt (deraedt@informatik.uni-freiburg.de), Stefan Wrobel (wrobel@iws.cs.uni-magdeburg.de)

TOPICS OF INTEREST

The topics of interest (listed in alphabetical order) include, but are not limited to, the following:

• 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

Saso Dzeroski, Luc De Raedt, and Stefan Wrobel