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Inventi Impact - Tech Research & Reviews

Articles

  • Inventi:etr/17825/15
    EFFECTUAL ANALYTICAL BIG UNCERTAIN DATA PROCESSING USING CT-PRO: A PROPOSAL
    Aditi Dilip Shinde*, S G Sutar

    In recent times the uncertain data snatched more attention of the data mining community. Big Data is a term which deals with the large amount of data which uses the techniques to capture, process, analyze and visualize the datasets. The most of the data generated by many applications is uncertain. So there is need to consider this big uncertain data for mining. To reduce the volumes of big data we may use the frequent items mining. By using the FIM the size of large data can be easily managed and this data is used for processing. Frequent itemset mining (FIM) is an essential part of association rules mining. It has been an active research area and a large number of algorithms have been developed for FIM. CT-PRO is classic variation of FP-Growth based upon compact tree structure. This algorithm is used to find the frequent patterns. This paper proposes an effectual analytical big uncertain data processing using CT-PRO algorithm. First we find the frequent itemsets from uncertain data then the CFP-Tree will be constructed from the frequent itemsets. The CFP-Tree will be mine to form desired patterns.

    How to Cite this Article
    Aditi Dilip Shinde, Sandeep G Sutar. Effectual Analytical Big Uncertain Data Processing using CT-PRO: A Proposal. Inventi Impact: Tech Research & Reviews, 2016(1):5-7, 2016.
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