From: Saso Dzeroski
Date: Wed, 12 Jun 2002 15:16:30 +0200
Subject: Relational Data Mining Summer School, Aug 17-18, Helsinki, Finland
Relational Data Mining Summer School
17 and 18 August 2002, Helsinki, Finland (Just before ECML/PKDD-2002)
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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