----- Forwarded message from Ivan Bruha <[EMAIL PROTECTED]> -----
Date: Tue, 23 Mar 2004 12:01:11 -0500 (EST)
From: Ivan Bruha <[EMAIL PROTECTED]>
Reply-To: Ivan Bruha <[EMAIL PROTECTED]>
CALL FOR PAPERS
Data Mining: Methodology and Applications
within
8th WSEAS International Conference CSCC
(Circuits, Systems, Communications, and Computers)
Vouliagmeni, Athens, Greece, 12-15 July 2004
Motivation:
Data Mining (DM) has become a very attractive discipline both
for research and industry within last few years. Its goal is to
extract "pieces" of knowledge from usually very large databases.
It portrays a sequence of procedures that have to be carried out
so as to derive reasonable and understandable results. One of
its components is an inductive process which induces the above
"pieces" of knowledge; usually it is machine learning (ML).
However, most of the machine learning algorithms require more or
less prepared data in a reasonable format. Therefore, 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 analysis starts. Moreover, many ML systems do not
easily allow processing of numerical attributes as well as
numerical (continuous) classes. Therefore, certain 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 perfect from the view of custom or commercial
applications. It is quite known that a concept description as a
result of an inductive process has to be usually post-processed.
Post-processing procedures usually include various pruning
routines, rule quality processing, rule filtering, rule
combination, or even knowledge integration. All these procedures
provide a kind of "symbolic filter" for noisy, imprecise, or
"non-user-friendly" knowledge derived 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. Usually, these tools exploit techniques that
are not genuinely logical, e.g., statistics, neural nets, and
others.
These reasons let us to launch this workshop. We would be
pleased to accept the papers submitted to the following related areas:
(i) Methodology:
Mapping data, Scaling learning algorithms to
large datasets, Discretization/fuzzification of numerical
attributes, Grouping of values of symbolic attributes,
Attribute (feature) mining, Processing of continuous
classes, Interpretation and explanation, Evaluation,
Knowledge combination and integration
(ii) Applications:
Medicine, Banking, Financing, Geography, and
all other practical disciples
This workshop provides an opportunity for researchers to learn
about the challenges and real problems in development and
applications of machine learning techniques.
Organizer:
Ivan Bruha
McMaster University, Dept. Computing & Software
Hamilton, Ont., Canada L8S 4K1
http://www.cas.mcmaster.ca/~bruha
Phone: +1-905-5259140 ext 23439
Fax: +1-905-5240340
Email: [EMAIL PROTECTED]
Program Committee:
Ivan Bruha, McMaster University, Dept. Computing & Software
Hamilton, Ont., Canada L8S 4K1
Email: [EMAIL PROTECTED]
A. (Fazel) Famili
Institute for Information Technology, National Research Council
of Canada, Ottawa, Canada K1A 0R6
Email: [EMAIL PROTECTED]
Lubomir Bakule
Institute of Information and Automatization of Czech Academy of Sciences
Prague, CzechLand
Email: [EMAIL PROTECTED]
Organization Notes:
There will be one invited talk on the workshop which will survey
the 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 posters. Maximum size is 6 (six) 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.
Submission:
Submit your paper by email to I. Bruha (see the address above);
the Postscript or PDF formats would be the most suitable ones.
Important Dates:
Deadline for submission: 15-Apr-2004
Notification: 30-Apr-2004
Camera-ready copy: 15-May-2004
