Main Reference Paperkluster- An Efficient Scalable Procedure for Approximating the Number of Clusters in Unsupervised Learning, Big Data Research, 2018 [Python]
  • An unsupervised clustering algorithm is dependent on identifying the number of clusters. The proposed kluster procedure iteratively applies four statistical cluster number approximation methods to small subsets of data. It recommends the most frequent and mean number of clusters resulted from the iterations as the potential optimum number of clusters.

Description
  • An unsupervised clustering algorithm is dependent on identifying the number of clusters. The proposed kluster procedure iteratively applies four statistical cluster number approximation methods to small subsets of data. It recommends the most frequent and mean number of clusters resulted from the iterations as the potential optimum number of clusters.

  • To identify the number of clusters in unsupervised learning.

  • Low computation time.

  • To achieve the scalability.

Aim & Objectives
  • To identify the number of clusters in unsupervised learning.

  • Low computation time.

  • To achieve the scalability.

  • The proposed kluster is applied datasets with higher dimensions.

Contribution
  • The proposed kluster is applied datasets with higher dimensions.

  • 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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