Main Reference PaperAn adaptive algorithm for anomaly and novelty detection in evolving data streams, Data Mining and Knowledge Discovery, 2018 [Java / Python / R]
  • This work proposes an algorithm named Growing Neural Gas-Adaptive which evolving data streams for the task of anomaly and novelty detection. It provides better adaptation, removal, and creation of neurons. In the method, a learning rate allows for a better adaptation of the neurons in stationary and non-stationary distributions. Then irrelevant neurons are removed from data distribution.

+ Description
  • This work proposes an algorithm named Growing Neural Gas-Adaptive which evolving data streams for the task of anomaly and novelty detection. It provides better adaptation, removal, and creation of neurons. In the method, a learning rate allows for a better adaptation of the neurons in stationary and non-stationary distributions. Then irrelevant neurons are removed from data distribution.

  • To remove the irrelevant neurons from evolving stream data.

  • To reduce the wasted computations.

+ Aim & Objectives
  • To remove the irrelevant neurons from evolving stream data.

  • To reduce the wasted computations.

  • The proposed algorithms enhance by adequately split and distribute the graph of neurons on multiple machines running in parallel.

+ Contribution
  • The proposed algorithms enhance by adequately split and distribute the graph of neurons on multiple machines running in parallel.

  • 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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