Association rule discovery techniques have gradually been adapt-ed to parallel systems in order to take advantage of the higher speed and greater storage capacity that they offer. The transition to a distributed memory system requires the partitioning of the database among the processors, a procedure that is generally carried out indiscriminately. However, for some techniques the nature of the database partitioning can have a pronounced impact on execution time and attention will be focused on one such algorithm, Fast Parallel Mining (FPM). A new algorithm, Data Allocation Algorithm (DAA), is presented that uses Principal Component Analysis to improve the data distribution prior to FPM.
CITATION STYLE
Manning, A. M., & Keane, J. A. (2001). Data allocation algorithm for parallel association rule discovery. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2035, pp. 413–420). Springer Verlag. https://doi.org/10.1007/3-540-45357-1_44
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