  In this work we are analyzing scalability of the heuristic algorithm we used
in the past to discover knowledge from multi-valued symbolic attributes in
fuzzy databases. The non-atomic descriptors, characterizing a single attribute
of a database record, are commonly used in fuzzy databases to reflect
uncertainty about the recorded observation. In this paper, we present
implementation details and scalability tests of the algorithm, which we
developed to precisely interpret such non-atomic values and to transfer (i.e.
defuzzify) the fuzzy tuples to the forms acceptable for many regular (i.e.
atomic values based) data mining algorithms. Important advantages of our
approach are: (1) its linear scalability, and (2) its unique capability of
incorporating background knowledge, implicitly stored in the fuzzy database
models in the form of fuzzy similarity hierarchy, into the
interpretation/defuzzification process.
