Main Reference PaperFinding tendencies in streaming data using Big Data frequent itemset mining, Knowledge-Based Systems, 2018 [Python/Apache Spark]
  • This work is proposed to discover tendencies using frequent itemset mining in continuous stream data. In the initialization phase, an enumeration tree which contains the supports and the transforming bounds for each item. When the itemset is frequent, all its descendants are calculated taking into account the nodes at the same level. If the new transactions are added or dropped from the widow, the enumeration tree must be recalculated.

Description
  • This work is proposed to discover tendencies using frequent itemset mining in continuous stream data. In the initialization phase, an enumeration tree which contains the supports and the transforming bounds for each item. When the itemset is frequent, all its descendants are calculated taking into account the nodes at the same level. If the new transactions are added or dropped from the widow, the enumeration tree must be recalculated.

  • To reduce the computational complexity.

  • To improve the speed of the algorithm.

Aim & Objectives
  • To reduce the computational complexity.

  • To improve the speed of the algorithm.

  • The proposed scheme extends to consider the co-occurrences of frequent items in data streams.

Contribution
  • The proposed scheme extends to consider the co-occurrences of frequent items in data streams.

  • 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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