Analytical Study for Pattern Mining In E-Commerce Data

Prof. S. P. Godse, Shilpi Singh, Anushree ., Shaikh Nafisa, Nikita Medidar


In today’s digital world data is been increasing like anything largely in unstructured file format. There are many data mining techniques are there to mine these files. But it is bit challenging affair to mine semi structured data like XML. Many researchers are engaged in mining rules from XML data using popular algorithms like Apriori. As Apriori creates large amount of candidate sets so it needs more processing space  and execution time is also high as it keep creating the candidate sets at every time ,this creates confusion in many researchers to choose Apriori  for large data sets. So our idea of extraction of rules from large datasets like Reuters using Eclat rule mining algorithm which uses intersection of transactions for generating candidate item sets. Our approach enhances the Eclat performance by enforcing comparative vertical power sets for creation of candidate item sets to enhance the quality of the rules with less processing space and also with less execution time.

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