Main Reference PaperFiDoop-DP: Data Partitioning in Frequent Itemset Mining on Hadoop Clusters, IEEE Transactions on Parallel and Distributed Systems, 2016 [Python/Hadoop]
  • This paper proposes a strategy to mitigate high communication and reduce computing cost in MapReduce-based FIM algorithms, the developed FiDoop-DP, which exploits correlation among transactions to partition a large dataset across data nodes in a Hadoop cluster.

Description
  • This paper proposes a strategy to mitigate high communication and reduce computing cost in MapReduce-based FIM algorithms, the developed FiDoop-DP, which exploits correlation among transactions to partition a large dataset across data nodes in a Hadoop cluster.

  • To improve the performance of Frequent Itemset Mining on Hadoop clusters.

  • To eliminate the redundant transactions.

  • To mitigate communication overhead.

  • Reducing computing cost.

Aim & Objectives

  • To improve the performance of Frequent Itemset Mining on Hadoop clusters.

  • To eliminate the redundant transactions.

  • To mitigate communication overhead.

  • Reducing computing cost.

  • To further reduce the computation cost and communication overhead in big data, in addition to similarity computation with includes importance to frequency of items.

Contribution
  • To further reduce the computation cost and communication overhead in big data, in addition to similarity computation with includes importance to frequency of items.

  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-Depending on the complexity of the project and requirements.

Order To Delivery
  • No Readymade Projects-Depending on the complexity of the project and requirements.

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