Parallelization of Fuzzy ARTMAP to Improve its Convergence Speed: The Network Partitioning Approach and the Data Partitioning Approach

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