Main Reference PaperAn efficient fuzzy c-means approach based on canonical polyadic decomposition for clustering big data in IoT, Future Generation Computer Systems, 2018 [Java]
  • Due to the large amount of data is collected from IoT, the fuzzy c-means algorithm is difficult to be executed in the IoT systems. To solve this issue, this work is proposed to utilize the tensor canonical polyadic decomposition to reduce the attributes of every object. Then fuzzy c-means algorithm is extended to a high-order fuzzy c-means method to make the clustering operations executed on the compressed objects in the tensor space.

Description
  • Due to the large amount of data is collected from IoT, the fuzzy c-means algorithm is difficult to be executed in the IoT systems. To solve this issue, this work is proposed to utilize the tensor canonical polyadic decomposition to reduce the attributes of every object. Then fuzzy c-means algorithm is extended to a high-order fuzzy c-means method to make the clustering operations executed on the compressed objects in the tensor space.

  • To reduce the attributes in large amounts of data.

  • To improve the clustering accuracy.

Aim & Objectives
  • To reduce the attributes in large amounts of data.

  • To improve the clustering accuracy.

  • An effective feature selection mechanism is contributed.

Contribution
  • An effective feature selection mechanism is contributed.

  • 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.

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Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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