Main Reference PaperMRQAR: a generic MapReduce framework to discover Quantitative Association Rules in Big Data problems, Knowledge-Based Systems, 2018 [Java/Hadoop]
  • A parallel framework is to discover the quantitative association rules in large amounts of data. The rule is constructed based on the MapReduce paradigm using Apache Spark. It performs an incremental learning, which is able to run any sequential quantitative association rule algorithm in Big Data problems.

Description
  • A parallel framework is to discover the quantitative association rules in large amounts of data. The rule is constructed based on the MapReduce paradigm using Apache Spark. It performs an incremental learning, which is able to run any sequential quantitative association rule algorithm in Big Data problems.

  • To find quantitative association rules in Big Data.

  • Reducing computational cost.

Aim & Objectives
  • To find quantitative association rules in Big Data.

  • Reducing computational cost.

  • An effective mechanism is developed to further reduce the search space in big data.

Contribution
  • An effective mechanism is developed to further reduce the search space in big data.

  • 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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