Main Reference PaperMapReduce Based Multilevel Consistent and Inconsistent Association Rule Detection from Big Data Using Interestingness Measures, Big Data Research, 2018 [Python/Hadoop]
  • The proposed frequent pattern mining algorithm is applied in two phases. In the first phase, multilevel association rules containing rule, interestingness measure and zone number are derived. In the second phase, the multilevel association rules are categorized into consistent and inconsistent rules for each zone.

Description
  • The proposed frequent pattern mining algorithm is applied in two phases. In the first phase, multilevel association rules containing rule, interestingness measure and zone number are derived. In the second phase, the multilevel association rules are categorized into consistent and inconsistent rules for each zone.

  • To achieve the minimum communication cost.

  • Reducing execution time.

Aim & Objectives
  • To achieve the minimum communication cost.

  • Reducing execution time.

  • The proposed scheme extends by considering the different weights for each interestingness measure and find weighted interesting multilevel association rules.

Contribution
  • The proposed scheme extends by considering the different weights for each interestingness measure and find weighted interesting multilevel association rules.

  • 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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