Abstract
In many domains of human activities it is now quite common to record huge sets of data in large data bases. It becomes a task of first importance to summarize these data in terms of their underlying concepts in order to extract new knowledge from them. These concepts can only be described by more complex type of data which we call symbolic data as they contain internal variation and they are structured. In this context, we have a rapidly increasing need to extend standard data analysis methods (exploratory, graphical representations, clustering, factorial analysis, discrimination,…) to these symbolic data.
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© 2000 Springer-Verlag Berlin Heidelberg
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Diday, E. (2000). Symbolic Data Analysis and the SODAS Project: Purpose, History, Perspective. In: Bock, HH., Diday, E. (eds) Analysis of Symbolic Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-57155-8_1
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DOI: https://doi.org/10.1007/978-3-642-57155-8_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66619-6
Online ISBN: 978-3-642-57155-8
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