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CALL FOR PAPERS
MRDM 2005 - 4th Workshop on Multi-Relational Data Mining
organised at the
11th ACM SIGKDD International Conference
on Knowledge Discovery & Data Mining
August 21 - 24, 2005, Chicago, IL, USA
Paper submissions due: June 10, 2005
Workshop Website: http://www-ai.ijs.si/SasoDzeroski/MRDM2005/
Workshop Contact: Saso Dzeroski (Saso.Dzeroski at ijs.si)
Workshop Date: August 21, 2005
Workshop chairs:
Saso Dzeroski (Saso.Dzeroski at ijs.si),
Hendrik Blockeel (Hendrik.Blockeel at cs.kuleuven.ac.be)
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 techniques.
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, ...
- Mining relational data from continuous streams
- Propositionalization methods for transforming (multi-)relational
data mining problems to single-table data mining problems
- Relational neural networks
- Relational pattern languages
- Statistical relational learning
We also encourage submissions which present early stages
of research work, software, and applications.
Saso Dzeroski, Hendrik Blockeel
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