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DM: ICML-99 workshop: CFP

From: Ivan Bruha
Date: Mon, 15 Mar 1999 12:50:42 -0500 (EST)

                  C A L L   F O R  P A P E R S
&&&&&&&  From Machine Learning to Knowledge Discovery in Databases 
                 :::::A WORKSHOP WITHIN:::::

                      (( ICML-99 ))

                Bled, Slovenia, 27-30 June 1999

     This workshop addresses an important aspect related to Knowledge 
in Databases (KDD) or Data Mining (DM) and Machine Learning (ML) in
pre-processing and analyzing real-world data.

     Knowledge Discovery in Databases has become a very attractive 
both for research and industry within last few years. Its goal is to 
"pieces" of knowledge or "patterns" from usually very large 
databases.  One of
its components is an inductive process which induces the above 
"pieces" of
knowledge; usually it is machine learning. However, most of the 
machine learning
algorithms require more or less prepared data in a reasonable format. 
some preprocessing routines as well as postprocessing ones should 
fill up the
entire chain of data processing.

     The data which are to be processed by an algorithm are usually 
noisy and
often inconsistent. Many steps are involved before the actual data 
starts. Moreover, many ML systems do not easily allow processing of 
attributes as well as numerical (continuous) classes. Therefore, 
procedures have to precede the actual data analysis process.

     Second, a result of an ML algorithm, such as a decision tree, a 
set of
decision rules, or weights and topology of a neural net, need not be 
from the view of custom or commercial applications. It is quite known 
a concept description as a result of an inductive process has to be
usually post-processed.  Post-processing procedures usually include 
pruning routines, rule quality processing, rule filtering, rule 
or even knowledge integration. All these procedures provide a kind of 
bolic filter" for noisy, imprecise, or "non-user-friendly" knowledge 
by an inductive algorithm.

     Thus, the pre- and post-processing tools always help the DM 
algorithms to
investigate databases as well as to refine the acquired knowledge. 
these tools exploit techniques that are not genuinely logical, e.g., 
tics, neural nets, and others.

     These reasons let us to launch this workshop. We would be 
pleased to
accept papers from the following areas:

o Mapping data
o Scaling learning algorithms to large datasets
o Handling noise
o Processing of unknown attribute values
o Discretization/fuzzification of numerical attributes
o Grouping of values of symbolic attributes
o Consistency checking
o Attribute (feature) selection and ordering
o Constructing new attributes
o Transforming attributes
o Processing of continuous classes
o Interpretation and explanation
o Evaluation
o Knowledge combination and integration

     This workshop provides an opportunity for researchers to learn 
about the
challenges and real problems in development and applications of 
learning techniques.


Ivan Bruha
McMaster University               
Dept. Computing & Software                 Phone: +1-905-5259140 ext 
Hamilton, Ont.                             Fax:   +1-905-5240340
Canada  L8S 4K1                            Email:

Marko Bohanec
Institut Jozef Stephan
Jamova 37
Ljubljana, Slovenia                        Email:

Program Committee:

A. (Fazel) Famili
Editor-in-Chief, Intelligent Data Analysis
Institute for Information Technology       Phone: +1-613-9938554
National Research Council of Canada,       Email:
Montreal Rd, Ottawa, Canada  K1A 0R6

Ivan Bruha, McMaster University, Hamilton, Canada

Marko Bohanec, Institut Jozef Stephan, Ljubljana, Slovenia

Stan Matwin, University of Ottawa, Information Technology and 
Ottawa, Ont., Canada  K1N 6N5
Email: stan@site.uottawa.ca5

Gholamreza Nakhaeizadeh, Daimler Benz AG, Germany

Igor Kononenko, Ljubljana Univ., Ljubljana, Slovenia

Petr Berka, Laboratory of Intelligent Systems, University of 
Prague, Czech Republic

W.F.S. (Skip) Poehlman, McMaster University, Hamilton, Canada

Organization Notes:

     There will be one invited talk on the workshop which will survey 
given topic as well as introduce own research.

     About upto 10 accepted papers will be presented (each 15-20 
min). If
there is a larger interest, then some papers might be accepted as 
Maximum size is 10 (ten) pages.

     Attendance is not limited to paper authors. However, in order to 
get an
early estimate of the possible attendance, we would appreciate an 
informal note
about your intention to attend. 

     A panel session at the end of the workshop will summarise what 
has been
learned from the workshop and will identify future directions. 

>>>>>>  Please note that authors of the best papers will be invited 
>to submit
an extended version of their papers to the Intelligent Data Analysis 
(, or even a special issue of the 
regarding this topic might be published.


     Submit your paper either by regular mail or by Email to I. Bruha 
the address above). If you use Email, then the Postscript format 
would be the
most suitable one.

Important Dates:

Deadline for submission: 30-Mar-99
Notification:            30-Apr-99
Camera-ready copy:       15-May-99

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