Main Reference PaperA new framework for metaheuristic-based frequent itemset mining, Applied Intelligence, 2018 [Java / Python / R]
  • To solve the intrinsic features of Frequent itemset mining (FIM) problem, this work proposes a framework, called META-GD. It is used to steer any metaheuristics-based FIM approach. Based on this framework, this work compares the performance of three new metaheuristic-based approaches, namely the genetic algorithm (GA), particle swarm optimization (PSO), and bee swarm optimization (BSO).

+ Description
  • To solve the intrinsic features of Frequent itemset mining (FIM) problem, this work proposes a framework, called META-GD. It is used to steer any metaheuristics-based FIM approach. Based on this framework, this work compares the performance of three new metaheuristic-based approaches, namely the genetic algorithm (GA), particle swarm optimization (PSO), and bee swarm optimization (BSO).

  • To find all frequent itemset from transaction data.

+ Aim & Objectives
  • To find all frequent itemset from transaction data.

  • A proposed framework is extended to exploit massive parallel computing capabilities to decrease the runtime of big data instances.

+ Contribution
  • A proposed framework is extended to exploit massive parallel computing capabilities to decrease the runtime of big data instances.

  • OS: Ubuntu 12.04 LTS 64bit

  • Language: Java/Python/R

+ Software Tools & Technologies
  • OS: Ubuntu 12.04 LTS 64bit

  • Language: Java/Python/R

  • 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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