CFP: MRDM Wshp @ SIGKDD-2003

[Saso Dzeroski]

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CALL FOR PAPERS
MRDM 2003 - 2nd Workshop on Multi-Relational Data Mining

organised at the

9th ACM SIGKDD International Conference
on  Knowledge Discovery & Data Mining
August 24 - 27, 2003, Washington DC, USA
Paper submissions due: 6 June 2003

Workshop Website: http://www-ai.ijs.si/SasoDzeroski/MRDM2003/
Workshop Contact: Saso Dzeroski ([EMAIL PROTECTED])
Workshop Date:    27 August 2003
Workshop chairs:
Saso Dzeroski ([EMAIL PROTECTED]),
Luc De Raedt ([EMAIL PROTECTED]),
Stefan Wrobel ([EMAIL PROTECTED])
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.
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
We also encourage submissions which present early stages
of research work, software, and applications.
Saso Dzeroski, Luc De Raedt, and Stefan Wrobel
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