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DM: PKDD 99 Call for participation

From: Milena Zeithamlova
Date: Sat, 26 Jun 1999 20:16:00 -0400 (EDT)

September 15-18, 1999, Prague, Czech Republic 

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'99 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. 

Both theoretical and applied contributions are sought. Of particular
interest is integration of ideas from different areas contributing to
KDD and elaboration of principles specific to KDD. The following list
exemplifies topics of interest:
- Data and knowledge representation for data mining

- Statistics and probability in data mining
- Logic-based perspective on data mining
- Data warehousing and knowledge discovery
- Man-Machine interaction in data mining
- Artificial Intelligence contributions to KDD
- High performance computing for data mining
- Machine learning and automated scientific discovery
- Quality assessment of data mining results
- Applications of data mining and knowledge discovery
- KDD process

About 80 both theoretical and applied papers covering all topics
of interest will be presented. The number of submitted papers grew
by 45% in comparison with PKDD '98  conference. 
The conference program will include:
* invited talks by KDD leaders and experts in the areas critical
  to the growth of KDD,
* oral and poster presentations of innovative research papers (list
  of them is given below),
* discovery systems demonstrations and hands-on experience in
  KDD applications,
* tutorials that provide quick and well-organized introduction
  to KDD and various application areas (list of them is given below),
* special sessions to present and discuss the results of Discovery 
  Challenge: exploration of several data bases available in advance
  to all conference participants.

Jan Zytkow, Univ. of North Carolina, Charlotte, e-mail:
Jan Rauch, University of Economics, Prague, e-mail:

Leonardo Carbonara, British Telecom, e-mail:

Petr Berka, University of Economics, Prague, e-mail:

For further program information please contact:
* by e-mail:
* or by regular mail: Jan Rauch, University of Economics,
   W.Churchill Sq..4, 130 67 Prague, Czech Republic

PKDD'99 will take place in Prague, Czech Republic, at the campus of
the University of Economics, W. Churchill Sq.. 4, Praha 3,
15 minutes walking distance from the historical center of Prague.

Those wishing to participate at the PKDD '99 are requested to fill in
the reply form which can be accessed at our Website
and to pay the registration and accommodation fees.
The deadline for the early registration is July 20, 1999.

For the registration and accommodation details do not hesitate
to contact the Action M Agency, Vrsovicka 68,101 00 Prague 10, 
Czech Republic
* by e-mail:
* by phone: +420 2 6731 2333-4
* by fax:+420 2 67310503

To assist us in planning the conference, please return the
Pre-registration form at your earliest convenience.
Having your mailing address, each pre-registered person will
obtain the printed version of the conference announcement.


to be returned to

I intend to attend the PKDD '99 conference: yes /no

Last name:      
First name:    
Mailing address:           

Arrival on September:                            Departure on 
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Name of accompanying person:         

Please send me printed announcement about PKDD '99 conference:

yes/ no

I would suggest to send the printed announcement also to the
following person:


REGULAR PAPERS(ordered by identification numbers):

Hendrik Blockeel, Saso Dzeroski, Jasna Grbovic:
Simultaneous prediction of multiple chemical parameters of river
water quality with TILDE 

Elisa Bertino, Catania Barbara, E. Caglio:
Applying Data Mining Techniques to Wafer Manufacturing 

Chris Clifton, Robert Cooley:
TopCat: Data Mining for Topic Identification in a Text Corpus 

Eamonn J. Keogh, Michael J. Pazzani:
Scaling up Dynamic Time Warping to Massive Datasets 

Tapio Elomaa, Juho Rousou:
Speeding up the search for optimal partitions 

Flexer Arthur:
On the use of self-organizing maps for clustering and visualization 

Robert J Hilderman, Howard J. Hamilton:
Heuristic Measures of Interestingness 

Ivanek Jiri:
On the Correspondence between Classes of Implicational and
Equivalence Quantifiers 

F. A. El-Mouadib, J. Koronacki, J. M. Zytkow:
Taxonomy Formation by Approximate Equivalence Relations Revisited 

Giuseppe Manco, Fosca Giannoti:
Querying Inductive Databases via Logic-Based User Defined Aggregates 

S. Massa, P.P. Puliafito:
An application of data mining to the problem of the University
students' drop-out using Markov chains 

Gou Masuda, Rei Yano, Norihiro Sakamoto, Kazuo Ushijima:
Discovering and Visualizing Attribute Associations using Bayesian
Networks and Their Use in KDD 

Andrzej Skowron, Hung Son Nguyen:
Boolean Reasoning Scheme with Some Applications in Data Mining 

Richard Nock, Marc Sebban, Pascal Jappy:
Experiments on a Representation-Independent `Top-down and
Prune'' Induction Scheme 

Ronan Pairceir, Sally McClean, Bryan Scotney:
Automated Discovery of Rules and Exceptions from Distributed
Databases Using Aggregates 

Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Joerg Sander:
OPTICS-OF: Identifying Local Outliers 

Sebban Marc, Richard Nock:
Contribution of Boosting in Wrapper Models 

Zbygniew R. Struzik, Arno Siebes:
The Haar Wavelet Transform in Time Series Similarity Paradigm 

Ljupco Todorovski, Saso Dzeroski:
Experiments in meta-level learning with ILP 

Shusaku Tsumoto:
Knowledge Discovery in Medical Multidatabases: A Rough Set Approach 

Shusaku Tsumoto:
Rule Discovery in Large Time-Series Medical Databases 

Segrei Levin, Alexander Tuzhilin:
Discovery of Association Rules in Gene Regulation Profiles 

Thomas Wittmann, Johannes Ruhland, Matthias Eichholz:
Enhancing Rule Interestingness for Neuro-Fuzzy Systems 

Dmitry Zelenko:
Optimizing Disjunctive Association Rules 

Ning Zhong, Satoshi Yamashita Y. Y. Yao:
Peculiarity Oriented Multi-Database Mining 

M. Sebban, D.A. Zighed, S. Di Palma:
Selection and Statistical Validation of Features and Prototypes 

Ronen Feldman, Yonatan Aumann, Moshe Fresko, Orly Liphstat,
Binyamin Rosenfeld, Yonatan Shler:
Text Mining Via Information Extraction

Uzi Murad, Gadi Pinkas:
Unsupervised Profiling for Identifying Superimposed Fraud 

Thomas Agotnes, Jan Komorowski, Terje Loken:
Taming Large Rule Models in Rough Set Approaches 

POSTERS (ordered by identification numbers):

Erick Alphonse, Celine Rouveirol:
Test Incorporation for propositionalization methods in ILP 

Henry Brighton, Chrish Mellish:
On the consistency of information filters for lazy learning 

Robert Cattral, Franz Oppacher, Dwight Deugo:
Using Genetic Algorithms to Evolve a Rule Hierarchy 

F. Coenen, G. Swinnen, K. Vanhoof, G. Wets:
T Improvement of Response Modelling: Combining Rule Induction
and Case-based Reasoning

Piew Datta:
Business Focused Evaluation Methods: A Case Study 

Wolfgang Ertel, Manfred Schramm:
Combining Data and Knowledge by MaxEnt-Optimization of
Probability Distributions 

Feelders Ad:
Handling missing data in trees: surrogate splits or statistical

Cristina S. Fertig, Alex A. Freitas, Lucia V. R. Arruda, Celso
A Fuzzy Beam-Search Rule Induction Algorithm 

Hajek Petr:
Logics for data mining (GUHA rediviva) 

Klaus Huber:
A Comparison of Model Selection Procedures for Predicting
Turning Points in Financial Time Series 

Jaturon Chattratichat:
A Visual Interface for Internet-based Data Mining and Knowledge

Ilhan Uysal, H. Altay Guvenir:
Regression by Feature Projections 

Claire J. Kennedy, Christophe Giraud-Carrier, Douglas W. Bristol:
Predicting Chemical Carcinogenesis Molecules using Structural
Information Only 

Mikhail V. Kiselev, Sergei M. Ananyan, Sergey B. Arseniev:
LA - a Clustering Algorithm with an Automated Selection of
Attributes, which is Invariant to Functional Transformations of

Mika Klemettinen, Heiki Mannila, A. Inkeri Verkamo:
Association Rule Selection in a Data Mining Environment 

Sergei O. Kuznetsov:
Learning of Conceptual Graphs from Positive and Negative

Wojciech Kwedlo, Marek Kretowski:
An evolutionary algorithm using multivariate discretization for
decision rule induction 

Stephane Lallich:
ZigZag, a New Clustering Algorithm to Analyze Categorical Variable
Cross-Classification Tables 

Jinyan Li, Xiuzhen Zhang, Guozhu Dong, Ramamohanarao Kotagiri,
Qun Sun:

Efficient Mining of High Confidence Association Rules without
Support Thresholds

Churn-Jung Liau, Duen-Ren Liu:
A Logical Approach to Fuzzy Data Analysis 

Guido Lindner, Rudi Studer:
AST: Support for Algorithm Selection with a CBR Approach 

Stefano Lodi, Luisella Reami, Claudio Sartori
Efficient Shared Near Neighbor Clustering of Large Metric Data

Ren=E9 MacKinney-Romero, Christophe Giraud-Carrier:
Learning from Highly Structured Data by Decomposition 

Javier Raymundo Garcia-Serrano, Jose Francisco Martnez-Trinidad:
Extension to C-means Algorithm for the Use of Similarity Functions

Maria C. Fernandez-Baizan, Ernestina Menasalvas Ruiz, Jose M.
Pena Sanchez, Socorro Millan, Eloina Mesa:
Rough Dependencies as a Particulare Case of Correlation:
Application to the Calculation of Approximate Reducts 

Michal Pechoucek, Olga Stepankova, Petr Miksovsky:
Maintenance of Discovered Knowledge 

Eddy Mayoraz, Miguel Moreiral:
Data Binarization for Logical Analysis 

Maybin K. Muyeba, John A. Keane:
Extending Attribute-Oriented Induction as a Key Preserving Data
Mining Method 

Nikolay Nikolaev, Hitoshi Iba:
Automated Discovery of Polynomials by Inductive Genetic

Aleksander Ohrn, Jan Komorowski:
Diagnosing Acute Appendicitis with Very Simple Classification Rules

Takashi Okada:
Rule Induction by Cascade Model based on Sum of Squares

J-M Petit, F. Toumani:
Discovery of Inclusion Dependencies Using a Workload of SQL

Xiaodong Chen, Ilias Petrounias:
Mining Temporal Features in Association Rules 

Clara Pizzuti, Domenico Talia, Giorgio Vonella:
A Divisive Initialization Method for Clustering Algorithms 

Lubos Popelinsky, Tomas Pavelek:
Mining lemma disambiguation rules from Czech corpora 

Sattiraju Prabhakar:
Compositional Constructive Induction: Discovering Topological
Features of Environmental Changes from Vision Data 

Chris P. Rainsford, John F. Roddicks:
Adding Temporal Interval Semantics to Association Rules 

R. Rakotomalala, S. Lallich, S. Di Palma:
Studying the behavior of generalized entropy in induction trees using
a M-of-N concept 

Zbigniew W. Ras:
Discovering Rules in Information Trees 

Andreas Rauber, Dieter Merkl:
Mining Text Archives: Creating Readable Maps to Structure and
Describe Document Collections 

Alexandr A. Savinov:
Mining Possibilistic Set-valued Rules By Generating Prime

Arno J. Knobbe, Arno Siebes, Daniel van der Wallen:
Multi-Relational Decision Tree Induction 

Dominik Slezak, Jakub Wroblewski:
Classification Algorithms Based on Linear Combinations of Features 

Myra Spiliopoulou:
The Notion of `Interesting Rule'' in Sequence Mining 

Andrzej Skowron, Jaroslaw Stepaniuk:
Towards Discovery of Information Granules 

Shinsuke Sugay, Einoshin Suzuki, Shusaku Tsumoto:
Support Vector Machines for Knowledge Discovery 

Johannes Ruhland, Thomas Wittmann:
Neuro-Fuzzy Data Mining for Target Group Selection in Retail

N. Xiong , L. Litz:
Generating Linguistic Fuzzy Rules for Pattern Classification with
Genetic Algorithms 

Zhiwei Fu:
An Innovative GA-Based Decision Tree Classifier in Large Scale
Data Mining 

Richard Cole, Peter Ecklund:
Analyzing an Email Collection Using Formal Concept Analysis 

Yonatan Aumann, Ronen Feldman, Yaron Ben Yehuda, David
Landau, Orly Liphstat, Yonatan Schler:
Circle Graphs: New Visualization Tools for Text-Mining 


Jan Mrazek:
Data Mining for Robust Business Intelligence Solutions 

Jean-Francois Boulicaut:
Query Languages for Knowledge Discovery Processes 

Michael Krieger and Susanne Kohler:
The ESPRIT Project CreditMine and its relevance for the internet

Petr Hajek and Jan Rauch:
Logics and Statistics for Association Rules and Beyond 

Myra Spiliopolou:
Data Mining for the Web 

Luc De Raedt and Hendrik Blockeel:
Relational learning and inductive logic programming made easy 

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