J. Castro, M. Georgiopoulos, J. Secretan, R. F. DeMara, G. Anagnostopoulos, and A. J. 
Gonzalez, "Parallelization of Fuzzy ARTMAP to Improve its Convergence Speed: The 
Boxing Approach and the Data Partitioning Approach," in Proceedings of the Fourth 
World Congress of Nonlinear Analysts (WCNA'04), Orlando, Florida, U.S.A., June 30 -
7, 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. We provide two methods for speeding up the FAM algorithm, 
the first one referred to as the Data Partitioning partitions the data into subsets for 
independent processing. The second one refered to as the Network partitioning approach 
uses a pipeline to distribute the work between processes during training. We provide 
experimental results on a Beowulf cluster of workstations for both approaches that 
confirm the merit of the modifications.