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Workshop settembe a Pisa SSDA at ECML/PKDD04
Dear SIS members
I am glad to announce you the following CALL for Paper of SSDA at
ECML/PKDD04
Antonio Irpino
CALL for Papers:
********************************************
SSDA at ECML/PKDD04
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Workshop on Symbolic and Spatial Data Analysis : Mining Complex Data
Structures
SEPTEMBER 20, 2004
PISA Italia
Workshop web site:
http://www.info.fundp.ac.be/ssda-pkdd04/>http://www.info.fundp.ac.be/ssda-pk
dd04/
Conference web site:
http://ecmlpkdd.isti.cnr.it/>http://ecmlpkdd.isti.cnr.it/
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IMPORTANT DATES
***************************
- Submission deadline: June 14, 2004
- Notification of acceptance: July 5, 2004
- Camera-ready copies due: July 12, 2004
WORKSHOP CHAIRS
********************
Monique Noirhomme-Fraiture (Institut d'Informatique, Namur - Belgique)
Paula Brito (Faculdade de Economia, University of Porto)
OBJECTIVE
**********
The main goal of this workshop is to bring together researchers from
different communities such as machine learning, data analysis, symbolic
data analysis and data mining to promote discussion and the development of
new ideas and methods to deal with complex structured data.
WORKSHOP STRUCTURE AND ATTENDANCE
**************************************
The workshop is intented to be a highly communicative meeting place for
researchers working on similar topics, but coming from different
communities. In order to achieve these goals, the workshop will consist of
invited talks, short presentations and discussions. Depending on the
number of attendees, participants may be asked to present themselves, and
state their research interests.
Depending on the submitted papers, sessions will be organized by topics.
At the end of each session, a discussion of the presented papers will take
place.
TOPICS
*******
The workshop will aim at a balance between theoretical issues and
descriptions of case studies. Topics of interest include but are not
limited to:
- Classification with Structured Data
- Complex Data Analysis
- Data Summarizing and Data Mining
- Mining Aggregated Data
- Spatial Data Analysis
- Symbolic Data Analysis
- Visualization of Complex Data
SUBJECT
********
SYMBOLIC DATA ANALYSIS
----------------------------------
There is an increasing need to extend standard exploratory, statistical
nd
graphical data analysis methods to the case of more complex data, that go
beyond the classical framework. This is the case of data concerning more
or
less homogeneous classes or groups of individuals (second-order objects or
macro-data), instead of single individuals (first-order objects or micro
data). The extension of classical data analysis techniques to the analysis
of second-order objects is one of the main goals of a novel research field
names "symbolic data analysis".
Symbolic data extend the classical tabular model, allowing multiple,
possibly weighted, values for each descriptive attribute which allow
representing variability and/or uncertainty present in the data. Symbolic
Data Analysis methods include univarite descriptive methods, clustering,
decision-tree, dicrimination, regression and factorial analysis
techniques,
which allow analysing symbolic data tables.
Symbolic data occur in many situations, for instance in summarising huge
sets of data or in describing the underlying concepts (a town, a
socio-demographic group, a scenario of accidents) of a database. It also
finds an important application field in official statistics; since by law,
NSI's are prohibited from releasing individual responses to any other
government agency or to any individual or business, data are aggregated
for
reasons of privacy before being distributed to external agencies and
institutes. Symbolic Data Analysis provides useful tools to analyse such
aggregated data.
Symbolic Data Analysis underwent great improvement with the European
projects "Symbolic Data Analysis Systems (SODAS)" and "Analysis System for
Symbolic Official Data (ASSO)"; as the result of these projects a software
package SODAS has been developed.
SPATIAL DATA ANALYSIS
-----------------------------
With the exponentially growing use of geographic information systems (GIS)
to store, manipulate and visualize geocoded information, it is
increasingly
important to understand the particular nature of geographic data and the
specialized techniques required for its analysis.
There is increasing interest in studying how techniques for the analysis
of
spatial data can be effectively applied in a GIS environment, such as the
study of spatial patterns and spatial autocorrelation, detection of
clusters, outliers and any other relationships that pertain to the
absolute
and relative location of observations.
Common applications of spatial data analysis techniques in the social
sciences range from the discovery of crime clusters, hot spots and the
detection of disease clusters, to spatial autocorrelations of demographic
variables and regression models for real estate analysis. Other
applications concernpublic health services searching for explanations of
disease clusters, environmental agencies assessing the impact of changing
land use patterns on climate change, geo-marketing companies doing
customer
segmentation based on spatial location, etc.
For supporting this type of analysis, most contemporary GIS have only very
basic spatial analysis functionalities; many are confined to analysis that
involves descriptive statistical displays, such as histograms or pie
charts. Data mining, which is the partially automated search for hidden
patterns in large databases, offers great potential benefits for the
applied GIS based decision making that takes place in public and private
sector organizations.
An up-to-date field of research
-----------------------------------------
Mining complex data structures is an up-to-date subject: research in the
related areas is developing rapidly, as shown by a great number of
contributions recently published on journals and success of workshops
organized on those topics. Moreover, several recent international
projects, financed by the European Union, have been devoted to the analysis
of
structured data. In this context, mention should be made to "ASSO -
Analysis System for Symbolic Official Data" and "SPIN - Spatial Mining for
Data of Public Interest".
SUBMISSIONS
************
See Also our WEB SITE http://www.info.fundp.ac.be/ssda-pkdd04/
Submission Procedure
--------------------------------
Authors are invited to submit original research contributions or
experience
reports in English. Submitted papers must be unpublished and substantially
different from papers under review. Papers that have been or will be
presented at small workshops/symposia whose proceedings are available only
to the attendees may be submitted.
Papers should be double-spaced and no longer than 5000 words (about 12
single-spaced pages). Papers should be sent electronically (postscript or
pdf) not later than June 14, 2004 .
Papers will be selected on the basis of review of full paper
contributions.
Notification of acceptance will be given by July 5, 2004 . Final
camera-ready copies of accepted papers will be due by July 12, 2004 . The
proceedings will be printed by the ECML/PKDD organizers and distributed at
the workshop. A web-publication of the proceedings is expected after the
conference. It is intended to publish a selection of papers as a special
number of an international journal.
Style Guide
-----------------
There is a joint paper style for the proceedings of all ECML/PKDD
workshops. Submitted papers should be formatted according to the
Springer-Verlag Lecture Notes in Artificial Intelligence guidelines.
Authors' instructions and style files can be downloaded from
http://www.springer.de/comp/lncs/authors.html.
PROGRAM COMMITTEE
********************
Annalisa Appice University of Bari, Italy
Patrice Bertrand ENST Brest , France
Lynne Billard University of Georgia, USA
Gilles Bisson IMAG, France
Hans-Hermann Bock Institute for Statistics RWTH Aachen , Germany
Carmen Bravo Univ. Complutense de Madrid , Spain
Marie Chavent Université de Bordeaux , France
Francisco De Carvalho Univ. Federal de Pernambuco, Brazil
Edwin Diday Université Paris-IX Dauphine , France
K. Chidananda Gowda University of Mysore , India
Georges Hebrail ENST, Paris , France
Manabu Ichino Tokyo Denki Unversity, Japan
Yves Lechevallier INRIA, France
Michèle Sebag Université Paris XI Orsay, France
Petko Valtchev University of Montreal , Canada
Rosanna Verde University of Naples II, Italy
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CONTACT
***********************************************************************
Paula Brito
Faculdade de Economia, University of Porto
Rua Dr. Roberto Frias, 4200-464 Porto, PORTUGAL
Tel : (+351) 225571233; Fax : (+351) 225505050
e-mail: mpbrito@fep.up.pt
Monique Noirhomme-Fraiture
Institut d'Informatique
Facultés Universitaires Notre Dame de la Paix
Rue Grandgagnage, 21, B-5000 Namur , BELGIUM
Tel : (+32) 81724979; Fax : (+32) 81724967
e-mail: monique.noirhomme@info.fundp.ac.be
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