Abstract
The need to extend standard data analysis methods (exploratory, clustering, factorial analysis, discrimination,…) to more complex data table is increasing in order to get more accurate information and summarize extensive data sets contained in huge databases. We define Symbolic Data Analysis (SDA) as the extension of standard Data Mining to such data tables.
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Diday, E., Lechevallier, Y. (2000). From Data Mining to Knowledge Mining: An Introduction to Symbolic Data Analysis. In: Gaul, W., Opitz, O., Schader, M. (eds) Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58250-9_12
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DOI: https://doi.org/10.1007/978-3-642-58250-9_12
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