Analysis of Symbolic Data

By H. Bock, E. Diday. 2000

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How researchers see this paper

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Data Mining Techniques (2001, background): “These characteristics may be symbolic objects (Bock and Diday 2000) including sets, intervals and frequency distributions, but in this discussion we will mainly contain our discussion to features which are either continuous variables or categorical.”
Principal component analysis: a review and recent developments (2016, background): “There is a recent body of work with so-called symbolic data, which is a general designation for more complex data structures, such as intervals or histograms [47,48].”
Modelling interval data with Normal and Skew-Normal distributions (2012, methodology): “Symbolic data analysis [5,7,11] provides a framework where new variable types allow to take directly into account variability and/or uncertainty associated to each single “individual”, avoiding restrictive summarizations to impose a fit to the classical representation structure.”
Centre and Range method for fitting a linear regression model to symbolic interval data (2008, methodology): “…of SDA is to provide suitable methods (clustering, factorial techniques, decision trees, etc.) for managing aggregated data described by multi-valued variables, for which the cells of the data table contain sets of categories, intervals or weight (probability) distributions (Bock and Diday, 2000).”