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                                               4th EUROPEAN CONFERENCE
                                         ON PRINCIPLES AND PRACTICE OF
                                      KNOWLEDGE DISCOVERY IN DATABASES
                                   LYON, FRANCE, September 13-16, 2000
http://eric.univ-lyon2.fr/~pkdd2000 (under construction)

About PKDD-2000
===============

Data Mining and Knowledge Discovery in Databases (KDD) have emerged
from a combination of many research areas: databases, statistics,
machine learning, automated scientific discovery, inductive logic
programming, artificial intelligence, visualization, decision
science, and high performance computing. While each of these areas
can contribute in specific ways, KDD focuses on the value that is
added by creative combination of the contributing areas. The goal of
PKDD-2000 is to provide a European-based forum for interaction among
all theoreticians and practitioners interested in data mining.
Interdisciplinary collaboration is one desired outcome, but the main
long-term focus is on theoretical principles for the emerging
discipline of KDD and on practical applications of discovery systems
that are built on those principles. We seek the KDD-specific
principles that go beyond each contributing area. We seek a new
generation of applications that go beyond applications developed in
each contributing area.


History
=======

PKDD 1997: Trondheim, Norway
PKDD 1998: Nantes, France
PKDD 1999: Prague, Czech Republic

Location
========

PKDD-2000 will be held at the University of Lyon 2
(http://www.univ-lyon2.fr/).
Lyon is the second city of France (1.2 M inhabitants).
The city of Lyon is a capital of the French gastronomy.
Communication facilities with high density communication network.
To know more about Lyon: http://www.lyon-france.com/


Topics of interest
==================

Data and knowledge representation for data mining
- Beyond relational databases: text, multimedia and web data models
- KDD-motivated data reduction and discretization
- Prior domain knowledge and use of discovered knowledge
- Knowledge representation for enterprise databases
- Modeling knowledge uncertainty
- Combining query systems with discovery capabilities

Statistics and probability in data mining
- Discovery of probabilistic networks
- Discovery of exceptions and deviations
- Pattern-recognition for data mining
- Statistical significance in large-scale search
- The problems of over-fit

Logic-based perspective on data mining
- Inference of knowledge from data
- Exploring different subspaces of first order logic
- Rough sets in data mining
- Boolean approaches to data mining
- Inductive Logic Programming for mining real databases
- The use of tolerance (similarity) relations in data mining

Data warehousing and knowledge discovery
- Data mining support for the design of a data warehouse
- Discovery techniques for data cleaning
- From data warehousing to knowledge warehousing
- Relations between OLAP and knowledge discovery
- Incremental knowledge representation for incremental data

Man-Machine interaction in data mining
- Visualization of data
- Visualization of knowledge
- User-friendly discovery interfaces
- Interactive data mining: human and computer contributions

Artificial Intelligence contributions to KDD
- Representing knowledge and hypotheses spaces
- Complexities of search for knowledge
- Combining many search methods in one system
- Data mining in distributed/multiagent systems

High performance computing for data mining
- Hardware support for KDD
- Parallel discovery algorithms and complexity
- Distributed data mining
- Scalability in high dimensional datasets
- Decomposition of large data tables

Machine learning and automated scientific discovery
- From concept learning to concept discovery
- Expanding the autonomy of data miners
- Embedding learning methods in KDD systems
- Conceptual clustering in knowledge discovery
- Applications of scientific discovery systems to databases
- Scientific hypothesis evaluation that transfers to KDD
- Hypothesis spaces of scientific discovery useful in KDD

Quality assessment of data mining results
- Multi-criteria knowledge evaluation
- Benchmarks and metrics for system evaluation
- Statistical tests in KDD applications
- Usefulness and risk assessment in decision-making

Applications of data mining and knowledge discovery
- Medicine: diagnosis and prognosis
- Control theory: predictive and adaptive control, model identification
- Engineering: diagnostics of mechanisms and processes
- Public administration
- Marketing and finance
- Data mining on the web in text and heterogeneous data
- Scientific databases
- Fraud detection

KDD process
- Discovery in enterprise databases
- Prediction and intervention use of knowledge
- Mining many databases
- New KDD algorithms

Program
=======

 - Invited talks by KDD leaders and experts
 - Oral and poster presentations of innovative research papers
 - Software demonstrations
 - Panel discussions
 - Discovery challenge
 - Tutorials


General Chair
=============

Jan Zytkow, (zytkow@uncc.edu) University of North Carolina, Charlotte (USA)

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