Traditional data mining software takes a flat
table as input
one attribute is the class that we want to
predict
the rest of the attributes are used by a classifier
to find a rule that explains the class
So, why is Dataconda different?
Dataconda does not consider a single flat
table,
but considers an entire relational database,
possibly made of many tables
The user selects the class attribute
and Dataconda will construct the best predictors
using the information in the whole database
The attribute generation algorithm allows
complex and unexpected patterns to emerge.
In this process, the user does not need to
write a single line of SQL
