Symbolic data analysis

Short description: Extension of standard data analysis

Symbolic data analysis (SDA) is an extension of standard data analysis where symbolic data tables are used as input and symbolic objects are made output as a result. The data units are called symbolic since they are more complex than standard ones, as they not only contain values or categories, but also include internal variation and structure. SDA is based on four spaces: the space of individuals, the space of concepts, the space of descriptions, and the space of symbolic objects. The space of descriptions models individuals, while the space of symbolic objects models concepts.[1][2]

## References

1. ↑ Diday, Edwin; Esposito (December 2003). "An introduction to symbolic data analysis and the SODAS software". Intelligent Data Analysis 7 (6): 583–601. doi:10.3233/IDA-2003-7606.
2. ↑ Lynne Billard; Edwin Diday (14 May 2012). Symbolic Data Analysis: Conceptual Statistics and Data Mining. John Wiley & Sons. ISBN 978-0-470-09017-6. https://books.google.com/books?id=F2sXfKNRvS0C.

## Further reading

* Diday, Edwin; Noirhomme-Fraiture, Monique (2008). Symbolic Data Analysis and the SODAS Software. Wiley–Blackwell. ISBN 9780470018835.

## External links

* Symbolic Data Analysis: Conceptual Statistics and Data Mining
* An introduction to symbolic data analysis and its Application to the Sodas Project by Edwin Diday

* R2S: An R package to transform relational data into symbolic data

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