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Parallel and Distributed Mining of Probabilistic Frequent Itemsets Using Multiple GPUs

  • Yusuke Kozawa
  • Toshiyuki Amagasa
  • Hiroyuki Kitagawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8055)

Abstract

Probabilistic frequent itemset mining, which discovers frequent itemsets from uncertain data, has attracted much attention due to inherent uncertainty in the real world. Many algorithms have been proposed to tackle this problem, but their performance is not satisfactory because handling uncertainty incurs high processing cost. To accelerate such computation, we utilize GPUs (Graphics Processing Units). Our previous work accelerated an existing algorithm with a single GPU. In this paper, we extend the work to employ multiple GPUs. Proposed methods minimize the amount of data that need to be communicated among GPUs, and achieve load balancing as well. Based on the methods, we also present algorithms on a GPU cluster. Experiments show that the single-node methods realize near-linear speedups.

Keywords

Graphic Processing Unit Load Balance Frequent Itemset Mining Existential Probability Uncertain Database 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yusuke Kozawa
    • 1
  • Toshiyuki Amagasa
    • 2
  • Hiroyuki Kitagawa
    • 2
  1. 1.Graduate School of Systems and Information EngineeringUniversity of TsukubaJapan
  2. 2.Faculty of Engineering, Information and SystemsUniversity of TsukubaJapan

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