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

Authors

  • Akanksha Bansal  Department of Computer Science & Engineering AITR, Indore, Madhya Pradesh, India
  • Dr. Amit Khare  Department of Computer Science & Engineering AITR, Indore, Madhya Pradesh, India
  • Rahul Moriwal  Department of Computer Science & Engineering AITR, Indore, Madhya Pradesh, India

DOI:

https://doi.org//10.32628/CSEIT206247

Keywords:

Data Mining, Frequent Pattern Mining, TID, ECLAT, Vertical Layout

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)

References

  1. R. Agrawal, T. Imielinski, and A.N. Swami, "Mining association rules between sets of items in large databases," in ACM SIGMOD International Conference on Management of Data, Washington, 1993.
  2. R. Agrawal, and R. Srikant, "Fast algorithms for mining association rules," in 20th International Conference on Very Large Data Bases, Washington, 1994.
  3. J. Han, J. Pei, and Y. Yin, "Mining frequent patterns without candidate generation," in ACM SIGMOD International Conference on Management of Data, Texas, 2000.
  4. M.J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, "New algorithms for fast discovery of association rules," in Third International Conference on Knowledge Discovery and Data Mining, 1997.
  5. a. K. G. M.J. Zaki, "Fast vertical mining using diffsets," in The nineth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003.
  6. Paul W. Purdom, Dirk Van Gucht, and Dennis P. Groth, "Average case performance of the apriori algorithm," vol. 33, p. 1223–1260, 2004.
  7. S. Orlando, P. Palmerini, R. Perego, and F. Silvestri, "Adaptive and resource-aware mining of frequent sets," in Proceedings of the 2002 IEEE International Conference on Data Mining, 2002.
  8. P. Shenoy, J.R. Haritsa, S. Sudarshan, G. Bhalotia, M. Bawa, and D. Shah, "Turbo-charging vertical mining of large databases," in ACM SIGMOD International Conference on Management of Data, 2000.
  9. B. Goethals, "Survey on frequent pattern mining," 2002. 
  10. Yan Zhang, Fan Zhang, Jason Bakos, "Frequent Itemset Mining on Large-Scale Shared Memory Machines," 2011.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2020-04-30

Issue

Volume 6, Issue 2, March-April-2020

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Akanksha Bansal, Dr. Amit Khare, Rahul Moriwal, " ECLAT Algorithm for Frequent Item Set Generation with Association Rule Mining Algorithm, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 2, pp.306-309, March-April-2020. Available at doi : https://doi.org/10.32628/CSEIT206247