Symbolic data analysis (SDA) is an extension of standard
data analysis
Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, en ...
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.
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References
Further reading
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External links
Symbolic Data Analysis: Conceptual Statistics and Data MiningAn 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
Data analysis
Computational statistics
Statistical programming languages
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