Static Load Balancing of Parallel Mining of Frequent Itemsets Using Reservoir Sampling

  • Robert Kessl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6871)


In this paper, we present a novel method for parallelization of an arbitrary depth-first search (DFS in short) algorithm for mining of all FIs. The method is based on the so called reservoir sampling algorithm. The reservoir sampling algorithm in combination with an arbitrary DFS mining algorithm executed on a database sample takes an uniformly but not independently distributed sample of all FIs using the reservoir sampling. The sample is then used for static load-balancing of the computational load of a DFS algorithm for mining of all FIs.


Association Rule Frequent Itemsets Sequential Algorithm Frequent Itemset Mining Coverage Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Robert Kessl
    • 1
  1. 1.Institute of Computer ScienceCzech Academy of SciencePrague 8Czech Republic

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