Asynchronous and Anticipatory Filter-Stream Based Parallel Algorithm for Frequent Itemset Mining

  • Adriano Veloso
  • Wagner MeiraJr.
  • Renato Ferreira
  • Dorgival Guedes Neto
  • Srinivasan Parthasarathy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3202)


In this paper we propose a novel parallel algorithm for frequent itemset mining. The algorithm is based on the filter-stream programming model, in which the frequent itemset mining process is represented as a data flow controlled by a series of producer and consumer components (called filters), and the data flow (communication) between such filters is made via streams. When production rate matches consumption rate, and communication overhead between producer and consumer filters is minimized, a high degree of asynchrony is achieved. Following this strategy, our algorithm employs an asynchronous candidate generation, and minimizes communication between filters by transferring only the necessary aggregated information. Another nice feature of our algorithm is a look forward approach which accelerates frequent itemset determination. Extensive evaluation shows the parallel performance and scalability of our algorithm.


Association Rule Parallel Algorithm Frequent Itemset Local Support Data Skewness 
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 2004

Authors and Affiliations

  • Adriano Veloso
    • 1
  • Wagner MeiraJr.
    • 1
  • Renato Ferreira
    • 1
  • Dorgival Guedes Neto
    • 1
  • Srinivasan Parthasarathy
    • 2
  1. 1.Computer Science DepartmentUniversidade Federal de Minas GeraisBrazil
  2. 2.Department of Computer and Information ScienceThe Ohio-State UniversityUSA

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