A Data Partitioning Approach to speed up the Fuzzy ARTMAP algorithm using the Hilbert space-filling Curve

One of the properties of FAM, which can be both an asset and a liability, 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. This same property though 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 training set into smaller sub-sets and have many FAMs trained, each one of them on a different training sub-set. This approach does not affect the classification performance or the network size. On the positive side, it speeds up the convergence time, required by FAM, to learn large datasets. 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