A data partitioning approach to speed up the fuzzy ARTMAP algorithm using the Hilbert space-filling curve

One of the properties of FAM, which is a mixed blessing, is its capacity to produce new neurons (templates) on demand to represent classification categories. This property allows FAM to automatically adapt to the database without having to arbitrarily specify network structure, but it also has the undesirable side effect that on large databases it can produce a large network size that can dramatically slow down the algorithms´ training time. To address this problem, we propose the use of the Hilbert space-filling curve. Our results indicate that the Hilbert space-filling curve can reduce the training time of FAM by partitioning the learning set without a significant effect on the classification performance or network size. Given that there is full data partitioning with the HSFC, we implement and test a parallel implementation on a Beowulf cluster of workstations that further speeds up the training and classification time on large databases.