J. Castro, M. Georgiopoulos, R. F. DeMara and A. J. Gonzalez, "A Data 
Partitioning Approach to speed up the Fuzzy ARTMAP algorithm using the Hilbert 
Space-Filling Curve," in Proceedings of the 2004 International Joint Conference 
on Neural Networks (IJCNN'04), Budapest, Hungary, July 25 - 29, 2004.

Abstract
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.