  For fast and efficient analysis of large sets of fuzzy data, elimination of
redundancies in the memory representation is needed. We used MTBDDs as the
underlying data-structure to represent fuzzy sets and binary fuzzy relations.
This leads to elimination of redundancies in the representation, less
computations, and faster analyses. We have also extended a BDD package (BuDDy)
to support MTBDDs in general and fuzzy sets and relations in particular.
Different fuzzy operations such as max, min and max-min composition were
implemented based on our representation. Effectiveness of our representation is
shown by applying it on fuzzy connectedness and image segmentation problem.
Compared to a base implementation, the running time of our MTBDD based
implementation was faster (in our test cases) by a factor ranging from 2 to 27.
Also, when the MTBDD based data-structure was employed, the memory needed to
represent the final results was improved by a factor ranging from 37.9 to
265.5.

Data Structures and Algorithms;Symbolic Computation