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Relational Data Mining Summer School
17 and 18 August 2002, Helsinki, Finland
(Just before ECML/PKDD-2002)
http://www-ai.ijs.si/SasoDzeroski/RDMSchool/
[Apologies if you receive multiple copies of this message.]
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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/
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