Parallelization of Fuzzy ARTMAP to improve its convergence speed: The network partitioning approach and the data partitioning approach

    Authors

    J. Castro; M. Georgiopoulos; J. Secretan; R. F. DeMara; G. Anagnostopoulos;A. Gonzalez

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Nonlinear Anal.-Theory Methods Appl.

    Keywords

    Fuzzy ARTMAP; BEOWULF parallel processing; Data partitioning; Network; partitioning; Mathematics, Applied; Mathematics

    Abstract

    One of the properties of FAM, which can be both an asset and a liability, is its capacity to produce newneurons (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 approach, partitions the data into subsets for independent processing. The second one, referred 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 speedup of the modifications. (C) 2005 Elsevier Ltd. All rights reserved.

    Journal Title

    Nonlinear Analysis-Theory Methods & Applications

    Volume

    63

    Issue/Number

    5-7

    Publication Date

    1-1-2005

    Document Type

    Article

    Language

    English

    First Page

    E877

    Last Page

    E889

    WOS Identifier

    WOS:000208147800088

    ISSN

    0362-546X

    Share

    COinS