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[Mlnet] MRDM @ KDD: deadline extension

From: Hendrik Blockeel <hendrik.blockeel@cs.kuleuven.ac.be>
Date: Mon, 13 Jun 2005 11:55:25 +0200
To: kdnet-members@ais.fraunhofer.de, mlnet@ais.fraunhofer.de
Message-Id: <200506131155.26005.hendrik.blockeel@cs.kuleuven.ac.be>

[Apologies if you receive this message more often than you would want to.]

*Submission deadline extended to June 22.*


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: EXTENDED to June 22, 2005

Workshop Website: http://www-ai.ijs.si/SasoDzeroski/MRDM2005/
Workshop Contact: Saso Dzeroski (Saso.Dzeroski@ijs.si)
Workshop Date:    August 21, 2005

Workshop chairs:
Saso Dzeroski (Saso.Dzeroski@ijs.si),
Hendrik Blockeel (Hendrik.Blockeel@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 


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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Received on Monday, 13 June 2005 12:31:29 GMT

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