Subject: Data Mining and Rough Sets Session
From: Wojtek Ziarko <[email-masked]>
To: [email-masked]
Date: Tue, 24 Oct 1995 17:39:53 -0600 (CST)

CALL FOR PAPERS
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			   CESA'96

      COMPUTATIONAL ENGINEERING IN SYSTEMS APPLICATIONS
              (Sponsored by IMACS and IEEE-SMC)

		      July 9-12, 1996

		        Lille, France 

                    INVITED SESSION ON
        
                 DATA MINING AND ROUGH SETS

Session Organizers:

Anita Wasilewska
Department of Computer Science
State University of New York at Stony Brook
Stony Brook, New York 11794-4400, USA
tel. (516) 632 8458
fax. (516) 632 8334
Internet:  [email-masked]

Wojciech Ziarko
Department of Computer Science
University of Regina
Regina, Saskatchewan, S4S 0A2, Canada
tel. (306) 585 5213
fax. (306) 585 4745
Internet: [email-masked]

Please, submit approximately one page abstract of your presentation by 
January 15, 1996 to Anita Wasilewska at the above address. Electronic 
submission is  recommended. Final, camera-ready papers will be due May 1,
1996. The papers will be  reviewed prior to inclusion in the Conference
Proceedings. 

About the Subject of the Session:   

Database mining is considered to be  one of the most promising directions
spanning database and AI research. The primary objective of database
mining methodologies and systems is to help the user in discovering
potentially significant facts or data patterns. Although statistical
methods offer some help in that respect, their applicability is limited
by often strong assumptions and general lack of techniques to analyze and 
characterize the structural relationships existing in data. Consequently,
much of database mining, or knowledge discovery in databases (KDD) research 
is rooted in former machine learning research. However, the goals of 
machine learning are different from database mining and, therefore, simple 
adaptation of learning algorithms  to deal with database mining questions 
solves a small part of the whole problem. What is needed is a comprehensive,
well founded and consistent approach aimed at dealing with a number of
KDD-related problems. Typical problems are the analysis of data dependencies,
identification of fundamental factors affecting such dependencies, discovery
of predictive rules etc. It seems that methodology of rough sets and its
extensions offer such a theoretical framework to systematically study
such KDD problems and related algorithms. 

The main subject of this session will be presentation of the most recent
research and applications of rough set-based approach to database mining.
We also look for contributions  covering related topics. These include
 neural nets approach and possibly other approaches to discovery of patterns 
in data, acquisition of control algorithms from data by using data mining 
techniques, data preprocessing techniques and techniques for approximate 
representation and reasoning with discovered knowledge.

Summary of Conference Deadlines:

January 15, 1996 
Submission of draft papers or extended abstracts

March 1, 1996
Notification of  acceptance

May 1, 1996
Submission of camera ready papers

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