Thinking by classes in Data Science: the symbolic data analysis paradigm
Résumé
Data Science, considered as a science by itself, is in general terms, the extraction
of knowledge from data. Symbolic data analysis (SDA) gives a new way of thinking
in Data Science by extending the standard input to a set of classes of individual
entities. Hence, classes of a given population are considered to be units of a
higher level population to be studied. Such classes often represent the real units
of interest. In order to take variability between the members of each class into
account, classes are described by intervals, distributions, set of categories or
numbers sometimes weighted and the like. In that way, we obtain new kinds of
data, called ‘symbolic’ as they cannot be reduced to numbers without losing
much information. The first step in SDA is to build the symbolic data table
where the rows are classes and the variables can take symbolic values. The second
step is to study and extract new knowledge from these new kinds of data by
at least an extension of Computer Statistics and Data Mining to symbolic data.
SDA is a new paradigm which opens up a vast domain of research and applications
by giving complementary results to classical methods applied to standard
data. SDA also gives answers to big data and complex data challenges as big data
can be reduced and summarized by classes and as complex data with multiple
unstructured data tables and unpaired variables can be transformed into a structured
data table with paired symbolic-valued variables.
Penser en terme de classes en Science des données