Main Reference PaperA Parallel MapReduce Algorithm to Efficiently Support Itemset Mining on High Dimensional Data, Big Data Research, 2018 [Java/Hadoop]
  • A parallel MapReduce based frequent closed itemset mining algorithm is proposed to efficiently parallelize the itemset extraction from extremely high-dimensional datasets. The proposed algorithm splits the depth first search process into a independent sub-processes. Each sub-process applies the centralized version of Carpenter on its conditional transposed table and extracts a subset of the final closed itemsets.

Description
  • A parallel MapReduce based frequent closed itemset mining algorithm is proposed to efficiently parallelize the itemset extraction from extremely high-dimensional datasets. The proposed algorithm splits the depth first search process into a independent sub-processes. Each sub-process applies the centralized version of Carpenter on its conditional transposed table and extracts a subset of the final closed itemsets.

  • Reducing the Execution time.

  • To find the frequent closed item-sets

  • To speed up the mining process.

Aim & Objectives
  • Reducing the Execution time.

  • To find the frequent closed item-sets

  • To speed up the mining process.

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

Contribution
  • To further reduce the computation cost and communication overhead in big data, in addition to similarity computation with includes the importance of 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-project delivery Depending on the complexity of the project and requirements.

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

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