@ARTICLE{Parthasarathy_01,
    AUTHOR      = {S. Parthasarathy and M. J. Zaki and M. Ogihara and W. Li},
    TITLE       = {{Parallel Data Mining for Association Rules on Shared-Memory Systems}},
    JOURNAL     = {{Knowledge and Information Systems}},
    YEAR        = {2001},
    VOLUME      = {3},
    NUMBER      = {1},
    PAGES       = {1-29},
    MONTH       = {February},
    NOTE        = {},
    KEYWORDS    = {},
    ISBN        = {},
    URL         = {http://portal.acm.org/citation.cfm?id=545301},
    ABSTRACT    = {In this paper we present a new parallel algorithm for data mining of
               association rules on shared-memory multiprocessors. We study the degree of
               parallelism, synchronization, and data locality issues, and present optimizations
               for fast frequency computation. Experiments show that a significant improvement of
               performance is achieved using our proposed optimizations. We also achieved good
               speed-up for the parallel algorithm.
                   A lot of data-mining tasks (e.g. association rules, sequential patterns) use
                   complex pointer-based data structures (e.g. hash trees) that typically suffer from
                   suboptimal data locality. In the multiprocessor case shared access to these data
                   structures may also result in false sharing. For these tasks it is commonly
                   observed that the recursive data structure is built once and accessed multiple times
                   during each iteration. Furthermore, the access patterns after the build phase are
                   highly ordered. In such cases locality and false sharing sensitive memory placement
                   of these structures can enhance performance significantly. We evaluate a set of
                   placement policies for parallel association discovery, and show that simple
                   placement schemes can improve execution time by more than a factor of two. More
                   complex schemes yield additional gains.},
}