Relational Data Mining School

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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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