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Relational Data Mining School (fwd)

From: Dan Brickley <danbri@w3.org>
Date: Fri, 14 Jun 2002 04:10:06 -0400 (EDT)
To: <www-rdf-interest@w3.org>
Message-ID: <Pine.LNX.4.30.0206140409090.31600-100000@tux.w3.org>

This looks like it might be interesting (re 'web mining' techniques...)  --dan

---------- Forwarded message ----------
Date: Thu, 13 Jun 2002 20:35:49 -0300
From: Saso Dzeroski <Saso.Dzeroski@ijs.si>
Reply-To: Inductive Learning Group <INDUCTIVE@LISTSERV.UNB.CA>
Subject: Relational Data Mining School
Resent-Date: Fri, 14 Jun 2002 01:27:17 +0100
Resent-From: owner-inductive@LISTSERV.UNB.CA
Resent-Subject: Relational Data Mining School


Relational Data Mining Summer School

17 and 18 August 2002, Helsinki, Finland
(Just before ECML/PKDD-2002)


[Apologies if you receive multiple copies of this message.]


Relational Data Mining (RDM) is the multi-disciplinary field dealing with
knowledge discovery from relational databases consisting of multiple tables.
To emphasize the contrast to typical data mining approaches that look for
patterns in a single database relation, the name Multi-Relational Data Mining
(MRDM) is often used as well. Mining data which consists of complex/structured
objects also falls within the scope of this field: 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 (ILP), KDD, data mining, machine learning and
relational databases; producing new techniques for mining multi-relational data;
and practical applications of such tecniques.

Present RDM 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. RDM 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. RDM 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 (e.g., information extraction from Web sources).

The Summer School on Relational Data Mining will provide
a comprehensive introduction to the techniques and applications
of relational data mining by leading experts in the field.
The Summer School is organized with the help and support of
the University of Helsinki and is financially supported by ILPnet2
(The Network of Excellence in Inductive Logic Programming).
Attendance will be free of charge, but registration is required.

More information at http://www-ai.ijs.si/SasoDzeroski/RDMSchool/

Received on Friday, 14 June 2002 04:10:07 UTC

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