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| 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 |
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Introduction |
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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
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). | |||||
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Program and Slides |
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| The slides of the lectures are now available for download. Copyrights
remain with the authors. |
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| Saturday, 17 Aug 2002: |
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8:50 Welcome |
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| 9:00 - 9:45 An introduction to relational data mining Saso Dzeroski |
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9:45 - 10:30 An introduction to inductive logic programming Nada Lavrac |
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10:30 - 11:00 Coffee break |
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| 11:00 - 11:45 Propositionalization as a way of understanding RDM and ILP Peter Flach |
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11:45 - 12:30 A methodology of ILP Luc De Raedt |
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12:30 - 14:00 Lunch break (lunch is on your own) |
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14:00 - 14:45 Logical trees for classification, regression and clustering Hendrik Blockeel |
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14:45 - 15:30 Relational subgroup discovery Stefan Wrobel |
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15:30 - 16:00 Coffee break |
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16:00 - 16:45 Relational distance-based methods Stefan Wrobel |
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16:45 - 17:30 Kernel-based learning from structured data Thomas Gaertner |
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| Sunday, 18 Aug 2002: |
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9:00 - 9:45 Learning statistical models from relational data Lise Getoor |
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9:45 - 10:30 Bayesian logic programs Kristian Kersting and Luc De Raedt |
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10:30 - 11:00 Coffee break |
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11:00 - 12:30 Applications of ILP/RDM to bioinformatics Ross King |
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12:30 - 14:00 Lunch break (lunch is on your own) |
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14:00 - 14:45 Miscellaneous applications of RDM Saso Dzeroski |
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14:45 - 15:30 Inductive databases Luc De Raedt |
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15:30 - 16:00 Coffee break |
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16:00 - 17:30 Future research / open issues in ILP/RDM Discussion/Panel/Most lecturers |
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