The data descriptions of the units are called
"symbolic" when they
are more complex than standard ones due to the fact that they
contain internal variation and are structured.

Symbolic data arise from many sources, for
instance in order to summarise huge Relational Databases by
their underlying concepts. "Extracting knowledge"
means getting explanatory results, that why, "symbolic
objects" are introduced. They model concepts and
constitute an ex-planatory output for data analysis. Moreover
they can be used in order to define queries of a Relational
Database and propagate concepts between Databases.

We define "Symbolic
Data Analysis" (SDA) as the extension of standard
Data Analysis to symbolic data tables as input in order to
find symbolic objects as output.

Any "Symbolic
Data Analysis" (SDA) is based on four spaces:
the space of individuals , the space of concepts, the space
of descriptions modeling individuals or classes of individuals,
the space of symbolic objects modeling concepts. Based on
these four spaces, new problems appear such as the quality,
robustness and reliability of the approximation of a concept
by a symbolic ob-ject, the symbolic description of a class,
the consensus between symbolic descriptions, etc.

******

**More information** can be found in the
general book on Symbolic Data Analysis: H.-H. Bock, E. Diday
(eds.): *Analysis of Symbolic Data: Exploratory Methods
for Extracting Statistical Information from Complex Data*
, Springer Verlag, 2000. [see also the section on JSDA
Journal]

And for a more practical introduction see,
for example, the articles on applications of Symbolic Objects
in Official Statistics by P. Calvo, M. Más and H. Olaeta
available in pdf format at http://www.eustat.es/document/ct_1_i.html
.