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Due to many requests, the paper submission
deadline has been extended to:

        Monday April 26, 2004
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Apologies for multiple posting
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Final Call for Papers

ECML/PKDD-2004
Pisa, Italy, September 20-24, 2004
http://ecmlpkdd.isti.cnr.it
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The 15th European Conference on Machine Learning (ECML) and the 8th European Conference
on Principles and Practice of Knowledge Discovery in Databases (PKDD) will be co-located
in Pisa, Italy, September 20-24, 2004. The combined event will comprise presentations of
contributed papers and invited speakers, a wide program of workshops and tutorials,
a demo session, and a discovery challenge.


Important dates

- *EXTENDED* Submission deadline: *Monday April 26, 2004*
- Notification of acceptance: Monday June 7, 2004
- Camera-ready copies due: Monday June 28, 2004
- Conferences: Monday September 20 through Friday September 24, 2004



Paper submission

High quality research contributions pertinent to any aspects of machine learning and
knowledge discovery are called for, ranging from principles to practice; particular
attention will be paid to papers describing innovative, challenging  applications.

There will be a single electronic submission procedure, where authors should indicate
whether they submit their paper to ECML, PKDD, or both. In the latter case, the topic
of the joint submission must be within the scope of both conferences; accepted joint
submissions will be assigned to the most appropriate of the conferences. Student
submissions should be clearly indicated on the submission form. All submissions will
be reviewed by the respective program committees.

The papers must  be in English and should be formatted according to the Springer-Verlag
Lecture Notes in Artificial Intelligence guidelines. Authors instructions and style
files can be downloaded at http://www.springer.de/comp/lncs/authors.html.  The maximum
length of papers is 12 pages. The proceedings of ECML and PKDD will be published as two
separate volumes by Springer-Verlag in the Lecture Notes in Artificial Intelligence
series and will be available at the conference.

Simultaneous submissions to other  conferences are allowed, provided this fact is clearly
indicated on the submission form. Simultaneous submissions that are not clearly specified
as such will be rejected.  Accepted papers will appear in the ECML/PKDD conference
proceedings only if they are  withdrawn from proceedings of other conferences.


Best Paper Awards

KDNet and Kluwer will honour the best papers and the best student papers with awards.
The awards will be based on the significance and originality of the contributions.


ECML Call for Papers

The European Conference on Machine Learning series intends to provide an international forum
for the discussion of the latest high quality research results in machine learning and is the
major European scientific event in the field. Submissions of papers that describe the application
of machine learning methods to real-world problems are encouraged, particularly exploratory
research that describes novel learning tasks and applications requiring non-standard techniques.
Submissions that demonstrate both theoretical and empirical rigor are especially encouraged.

Topics of interest (non-exhaustive list):

- artificial neural networks
- Bayesian networks
- case-based reasoning
- computational models of human learning
- computational learning theory
- cooperative learning
- decision trees
- discovery of scientific laws
- evolutionary computation
- evaluation metrics and methodologies
- grammatical inference
- incremental induction and on-line learning
- inductive logic programming
- information retrieval and learning
- instance based learning
- kernel methods
- knowledge acquisition and learning
- knowledge base refinement
- knowledge intensive learning
- learning from text and web
- machine learning of natural language
- meta learning
- multi-agent learning
- multi-strategy learning
- multirelational learning
- planning and learning
- reinforcement learning
- revision and restructuring
- statistical approaches
- statistical relational learning
- unsupervised learning
- vision and learning



PKDD Call for Papers

Data Mining and Knowledge Discovery in Databases (KDD) is the ability to extract useful patterns from
typically large amounts of data stored in databases, data warehouses or other information repositories.
KDD is a combination of many research areas: databases, statistics, machine learning, automated scientific
discovery, artificial intelligence, visualization, and high performance computing. KDD focuses on the
value that is added by the creative combination of the contributing areas. The European Conference on
Principles and Practice of Knowledge Discovery in Databases series intends to provide an international
forum for the discussion of the latest high quality research results in KDD and is the major European
scientific event in the field. Submissions are invited that describe empirical and theoretical research
in all areas of KDD, as well as submissions that describe challenging applications of KDD.

Topics of interest (non-exhaustive list):

Algorithms and techniques
- classification
- clustering
- frequent patterns
- rule discovery
- statistical techniques and mixture models
- constraint-based mining
- incremental algorithms
- scalable algorithms
- distributed and parallel algorithms
- privacy preserving data mining
- multi-relational data mining
Data mining and databases
- database integration
- inductive databases
- data mining query languages
- data mining query optimization
Data pre-processing
- dimensionality reduction
- data reduction
- discretization
- uncertain and missing information handling
Foundations of data mining
- complexity issues
- knowledge (pattern) representation
- global vs. local patterns
- logic for data mining
- statistical inference and probabilistic modelling
Innovative applications
- mining bio-medical data
- web content, structure and usage mining
- semantic web mining
- mining governmental data, mining for the public administration
- personalization
- adaptive data mining architectures
- invisible data mining
KDD process and process-centric data mining
- models of the KDD process
- standards for the KDD process
- background knowledge integration
- collaborative data mining
- vertical data mining environments
Mining different forms of data
- graph, tree, sequence mining
- semi-structured and XML data mining
- text mining
- temporal, spatial, and spatio-temporal data mining
- data stream mining
- multimedia mining
Pattern post-processing
- quality assessment
- visualization
- knowledge interpretation and use


Sponsors - initial list

ISTI-CNR, University of Pisa, INSA Lyon, University of Bari, Kluwer, KD-net, Springer


Committee and Chairs

Program Chairs:

Jean-François Boulicaut, INSA Lyon, France
Floriana Esposito University of Bari, Italy
Fosca Giannotti KDDLab, ISTI-CNR, Pisa, Italy
Dino Pedreschi KDDLab, University of Pisa, Italy

Workshop Chairs:

Donato Malerba, University of Bari, Italy
Mohammed J. Zaki, Rensselaer Polytechnic Institute, USA

Tutorial Chairs:

Katharina Morik, University of Dortmund, Germany
Franco Turini, KDDLab, University of Pisa, Italy

Discovery Challenge Chairs:

Petr Berka, Prague University of Economics, Czech Republic
Bruno Cremilleux, University of Caen, France

Publicity Chair:

Salvatore Ruggieri, KDDLab, University of Pisa, Italy

Demostration Committee:

Elena Baralis, Politecnico of Torino, Italy
Codrina Lauth, Fraunhofer AiS, Germany
Rosa Meo, University of Torino, Italy

Steering Committee:

Hendrik Blockeel, Katholieke Universiteit Leuven, Belgium
Luc De Raedt, Albert-Ludwigs University Freiburg, Germany
Tapio Elomaa, Tampere University of Technology, Finland
Peter Flach, University of Bristol, UK
Dragan Gamberger, Rudjer Boskovic Institute, Croatia
Nada Lavrac, Jozef Stefan Institute, Slovenia
Heikki Mannila, Helsinki Institute for Information Technology, Finland
Arno Siebes, Utrecht University, The Netherlands
Ljupco Todorovski, Jozef Stefan Institute, Slovenia
Hannu T.T. Toivonen, University of Helsinki, Finland

Award Committee:

Floriana Esposito (PC representative)
Robert Holte, University of Alberta, Canada (Kluwer representative)
Michael May, Fraunhofer AiS, Germany (KDNet representative)

Organizing Committee:

Maurizio Atzori KDDLab, ISTI-CNR, Pisa, Italy
Miriam Baglioni, KDDLab, University of Pisa, Italy
Sergio Barsocchi KDDLab, ISTI-CNR, Pisa, Italy
Jérémy Besson, INSA-Lyon, France
Francesco Bonchi, KDDLab, ISTI-CNR, Pisa, Italy
Stefano Ferilli, University of Bari, Italy
Tiziana Mazzone, KDDLab, Pisa,  Italy
Mirco Nanni, KDDLab, ISTI-CNR, Pisa, Italy
Ruggero Pensa, INSA-Lyon, France
Chiara Renso, KDDLab, ISTI-CNR, Pisa, Italy
Salvatore Rinzivillo, KDDLab, University of Pisa, Italy