Main Reference PaperUsing hashing and lexicographic order for Frequent Itemsets Mining on data streams, Journal of Parallel and Distributed Computing, 2018 [Python]
  • The proposed work is focused on discovering frequent itemsets in data streams composed of short transactions in large alphabets.In preprocessing stage, the top-k frequent 1-itemsets are extracted. The received transactions are handled using hash functions, and the lexicographic order of items is used for obtaining frequent itemsets.

Description
  • The proposed work is focused on discovering frequent itemsets in data streams composed of short transactions in large alphabets.In preprocessing stage, the top-k frequent 1-itemsets are extracted. The received transactions are handled using hash functions, and the lexicographic order of items is used for obtaining frequent itemsets.

  • To discover the frequent patterns in data streams.

  • To decrease the processing time of the algorithm.

Aim & Objectives
  • To discover the frequent patterns in data streams.

  • To decrease the processing time of the algorithm.

  • A technique is contributed that reduces the search space in frequent pattern mining.

Contribution
  • A technique is contributed that reduces the search space in frequent pattern mining.

  • 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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