|Multi-Relational Data Mining (MRDM) is the multi-disciplinary
field dealing with knowledge discovery from relational databases consisting
of multiple tables. Mining data which consists of complex/structured objects
also falls within the scope of this field, since 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, machine learning and relational databases; producing
new techniques for mining multi-relational data; and practical applications
of such tecniques.
Typical data mining approaches look for patterns in a single relation
of a database. For many applications, squeezing data from multiple relations
into a single table requires much thought and effort and can lead to loss
of information. An alternative for these applications is to use multi-relational
data mining. Multi-relational data mining can analyze data from a multi-relation
database directly, without the need to transfer the data into a single
table first. Thus the relations mined can reside in a relational or deductive
database. Using multi-relational data mining it is often also possible
to take into account background knowledge, which often corresponds to views
in the database.
Present MRDM 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. MRDM 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.
MRDM 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 (information extraction from text and Web sources).
The aim of the workshop is to bring together researchers and practitioners
of data mining interested in methods for finding patterns in expressive
languages from complex / multi-relational / structured data and their applications.
Why the topic is of interest?
An increasing number of data mining applications involve the analysis of
complex and structured types of data (such as sequences in genome analysis,
HTML and XML documents) and require the use of expressive pattern languages.
There is thus a clear need for multi-relational data mining (MRDM) techniques.
On the other hand, there is a wealth of recent work concerned with upgrading
some recent successful data mining approaches to relational logic. A case
in point are kernel methods (support-vector machines): the development
of kernels for structured and richer data types is a hot research topic.
Another example is the development of probabilistic relational representations
and methods for learning in them (e.g., probabilistic relational models,
first-order Bayesian networks, stochastic logic programs, etc.)
Non-exclusive list of topics. listed in alphabetical order:
Applications of (multi-)relational data mining
Data mining problems that require (multi-)relational methods
Distance-based methods for structured/relational data
Kernel methods for structured/relational data
Learning in probabilistic relational representations
Link analysis and discovery
Methods for (multi-)relational data mining
Mining structured data, such as amino-acid sequences, chemical compounds,
HTML and XML documents,
Propositionalization methods for transforming (multi-)relational data mining
problems to single-table data mining problems
Relational neural networks
Relational pattern languages
Contact information of organizers
Program Committee Members
Hendrik Blockeel (Katholieke Universiteit Leuven)
Jean-Francois Boulicaut (University of Lyon)
Diane Cook (University of Texas at Arlington)
Mark Craven (University of Wisconsin at Madison)
Luc Dehaspe (PharmaDM)
Pedro Domingos (University of Washington)
Peter Flach (University of Bristol)
Lise Getoor (University of Maryland)
David Jensen (University of Massachusets at Amherst
Ross King (University of Aberystwith)
Stefan Kramer (Albert-Ludwigs-Universitaet Freiburg)
Nada Lavrac (Jozef Stefan Institute)
Donato Malerba (University of Bari)
Heikki Mannila (Nokia Research / Helsinki Institute for Information Technology)
Tom Mitchell (Carnegie Mellon University)
Hiroshi Motoda (University of Osaka)
Stephen Muggleton (Imperial College)
David Page (University of Wisconsin at Madison)
Foster Provost (Stern School of Business, New York University)
Celine Rouveirol (University Paris Sud XI)
Gunter Saake (Otto-von-Guericke Universitaet Magdeburg)
Michele Sebag (University Paris Sud XI)
Arno Siebes (Universiteit Utrecht)
Hannu Toivonen (University of Helsinki / Nokia Research)
Submissions due: May 27, 2002
Notification: June 10, 2002
Camera ready: June 24, 2002
Workshop day: July 23, 2002
Discovering Knowledge from Relational Data Extracted from Business News.
A. Bernstein, S. Clearwater, S. Hill, C. Perlich and F. Provost.
H. Blockeel, M. Bruynooghe, S. Dzeroski, J. Ramon, and J, Struyf.
Statistical Models for Relational Data.
L. Getoor, D. Koller, and B. Taskar.
Schemas and Models.
D. Jensen and J. Neville.
Concept Formation Using Graph Grammars.
I. Jonyer, L. B. Holder, and D. J. Cook.
Constraint Based Mining of First-Order Sequences in SeqLog.
S. D. Lee and L. De Raedt.
Experiments With MRDTL -- A Multi-Relational Decision Tree Learning Algorithm.
H. Leiva and V. Honavar.
Mining Patterns from Structured Data by Beam-wise Graph-Based Induction.
T. Matsuda, H. Motoda, T. Yoshida, and T. Washio.
Structural Logistic Regression: Combining Relational and Statistical Learning.
A. Popescul, L.H. Ungar, S. Lawrence, and D.M. Pennock.