Summer School on

Relational Data Mining



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

Organized by: Saso Dzeroski and Bernard Zenko (program), Tapio Elomaa (local)


Program and Slides | Photos | Report













Introduction








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 relation of a database, 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, 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 provided a comprehensive introduction to the techniques and applications of relational data mining by leading experts in the field. The Summer School was organized by the Jozef Stefan Institute, Ljubljana, with the help and support of the University of Helsinki. It was financially supported by ILPnet2 (The Network of Excellence in Inductive Logic Programming).











Program and Slides








The slides of the lectures are now available for download. Copyrights remain with the authors.







Saturday, 17 Aug 2002:




8:50 Welcome




9:00 - 9:45
An introduction to relational data mining
Saso Dzeroski




9:45 - 10:30
An introduction to inductive logic programming
Nada Lavrac





10:30 - 11:00 Coffee break




11:00 - 11:45
Propositionalization as a way of understanding RDM and ILP
Peter Flach




11:45 - 12:30
A methodology of ILP
Luc De Raedt





12:30 - 14:00 Lunch break (lunch is on your own)




14:00 - 14:45
Logical trees for classification, regression and clustering
Hendrik Blockeel




14:45 - 15:30
Relational subgroup discovery
Stefan Wrobel





15:30 - 16:00 Coffee break




16:00 - 16:45
Relational distance-based methods
Stefan Wrobel




16:45 - 17:30
Kernel-based learning from structured data
Thomas Gaertner




Sunday, 18 Aug 2002:







9:00 - 9:45
Learning statistical models from relational data
Lise Getoor




9:45 - 10:30
Bayesian logic programs
Kristian Kersting and Luc De Raedt





10:30 - 11:00 Coffee break




11:00 - 12:30
Applications of ILP/RDM to bioinformatics
Ross King




12:30 - 14:00 Lunch break (lunch is on your own)




14:00 - 14:45
Miscellaneous applications of RDM
Saso Dzeroski




14:45 - 15:30
Inductive databases
Luc De Raedt





15:30 - 16:00 Coffee break




16:00 - 17:30
Future research / open issues in ILP/RDM
Discussion/Panel/Most lecturers












Bernard Zenko
Created: June 2002, Updated: October 2002