Title: Analysis of Symbolic and Structured
Data
Description:
With the advent of the “information age”, we have
witnessed a dramatic
growth of applications in government, business and education, many of
which are sources of various data, organised in different structures
and formats. As a consequence, there is an increasing need to extend
standard exploratory, statistical and graphical data analysis methods
to more complex data, that go beyond the classical framework, which is
characterized by a relatively simple representation of data, such as a
database relation or a standard data table.
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 named 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 univariate descriptive methods, clustering, decision-tree,
discrimination, regression and factorial analysis techniques, which
allow analysing symbolic data tables.
A particular type of structured data is represented by taxonomic
attributes, that is, attributes whose categories are ordered in a
rooted hierarchical tree, called taxonomy. On the other hand,
dependencies may exist between variables. These dependencies may be
logical (e.g. if colour is blue then type is river), causal (e.g. if
driving speed is high, then the number of accidents is high with
probability 0.8) or hierarchical, expressing that the applicability of
one variable depends on the values taken by another one (e.g. (if
gender is male then the number of pregnancies is non-applicable). A
more complex representation is given by first-order logic, where both
attributes of single individuals and relations between individuals are
represented.
Structured data arise from many different domains, such as official
statistics, for the handling of census data, survey data, where
questions are often dependent on each other, data warehouses, GIS
applications, XML documents or genomic databases.
This track is meant to present contributions and stimulate discussion
on the statistics and analysis of symbolic and structured data.
Co-Chairs:
Paula Brito
Faculdade de Economia
University of Porto
Rua Dr. Roberto Frias
4200-424 Porto
Portugal
Fax : (+351) 225505050
e-mail : mpbrito@fep.up.pt
|
Lynne Billard
University of Georgia at Athens
Statistics
102 Statistics Building
Athens, Georgia, USA
e-mail: lynne@stat.uga.edu |
Edwin Diday
LISE-Ceremade
Université Paris-IX Dauphine
Pl. du M.al de Lattre de Tassigny
75016 Paris, France
e-mail : diday@ceremade.dauphine.fr |
Georges Hébrail
Ecole Nationale Supérieure des Télécommunications
Département Informatique et Réseaux
46, rue Barrault
75634 Paris Cedex 13, France
e-mail : hebrail@enst.fr |
Donato Malerba
Dipartimento di Informatica
Università degli Studi di Bari
Via Orabona 4
70126 Bari, Italy
e-mail: malerba @ di.uniba.it |
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