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ECLAT Algorithm for Frequent Item Set Generation with Association Rule Mining Algorithm

Author

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  • Akanksha Bansal
  • Amit Khare
  • Rahul Moriwal

Abstract

Eclat is a program for frequent item set mining, a data mining method that was originally developed for market basket analysis. Frequent item set mining aims at finding regularities in the shopping behavior of the customers of supermarkets, mail-order companies and online shops. In particular, it tries to identify sets of products that are frequently bought together. Once identified, such sets of associated products may be used to optimize the organization of the offered products on the shelves of a supermarket or the pages of a mail-order catalog or web shop, may give hints which products may conveniently be bundled, or may allow suggesting other products to customers. However, frequent item set mining may be used for a much wider variety of tasks, which share that one is interested in finding regularities between (nominal) variables in a given data set. For an overview of frequent item set mining in general and several specific algorithms (including Eclat)

Suggested Citation

  • Akanksha Bansal & Amit Khare & Rahul Moriwal, 2020. "ECLAT Algorithm for Frequent Item Set Generation with Association Rule Mining Algorithm," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 6(2), pages 306-309, April.
    • Handle: RePEc:jbh:ijsrcs:v6:y2020:i2:id:hcseit206247
    DOI: 10.32628/CSEIT206247
    Note: Article URL: https://ijsrcseit.com/CSEIT206247
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