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.

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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 .