CFP last date
20 May 2024
Reseach Article

Frequent Itemset Mining in Data Mining: A Survey

by Rana Ishita, Amit Rathod
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 139 - Number 9
Year of Publication: 2016
Authors: Rana Ishita, Amit Rathod

Rana Ishita, Amit Rathod . Frequent Itemset Mining in Data Mining: A Survey. International Journal of Computer Applications. 139, 9 ( April 2016), 15-18. DOI=10.5120/ijca2016909219

@article{ 10.5120/ijca2016909219,
author = { Rana Ishita, Amit Rathod },
title = { Frequent Itemset Mining in Data Mining: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 139 },
number = { 9 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 15-18 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016909219 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:40:28.414529+05:30
%A Rana Ishita
%A Amit Rathod
%T Frequent Itemset Mining in Data Mining: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 139
%N 9
%P 15-18
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Data mining is process of extracting useful information from different perspectives. Frequent Item set mining is widely used in financial, retail and telecommunication industry. The major concern of these industries is faster processing of a very large amount of data. Frequent item sets are those items which are frequently occurred. So we can use different types of algorithms for this purpose. Frequent Item set mining can be performed Apriori, FP-tree, Eclat, and RARM algorithms. For the work in this paper, we have analyzed widely used algorithms for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori, Frequent Pattern (FP) Growth, Rapid Association Rule Mining (RARM) and ECLAT algorithm frequent pattern mining algorithms. This study also focuses on each of the algorithm’s advantages, disadvantages and limitations for finding patterns among large item sets in database systems.

  1. Shamila Nasreen, Muhammad Awais Azamb, Khurram Shehzad, Usman Naeem, Mustansar Ali Ghazanfar “Frequent Pattern mining algorithm finding associated frequent patterns for Data Streams: A Survey” 2014, Science Direct
  2. Xiaofeng Zheng a, Shu Wang a* “Study on the Method of Road Transport Management Information Data mining Based on Pruning Eclat Algorithm and Map Reduce “2014, Science Direct
  3. Zhigang Zhang, Genlin Ji*, Mengmeng Tang “MREclat: an Algorithm for Parallel Mining Frequent Item sets” 2013, IEEE
  4. Marghny H. Mohamed • Mohammed M. Darwieesh “Efficient mining frequent item sets algorithm” 2013, Springer
  5. Dr. S.Vijayarani, Ms. P. Sathya “An Efficient Algorithm for Mining Frequent Item Sets in Data Streams” 2013, International Journal of Innovative Research in Computer and Communication Engineering
  6. Kan Jin “A new Algorithm for Discovering Association Rules” 2010, IEEE
  7. Mingjun Song, and Sanguthevar Rajasekaran “A Transaction Mapping Algorithm for Frequent Item Sets Mining” Member, IEEE
  8. 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.
  9. R. Agrawal, and R. Srikant,"Fast algorithms for mining association rules," in 20th International Conference on Very Large Data Bases, Washington, 1994.
  10. J. Han, J. Pei, and Y. Yin, "Mining frequent patterns without candidate generation," in ACM SIGMOD International Conference on Management of Data, Texas, 2000.
  11. 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.
  12. Avani M. Sakhapara, Bharathi H. N. “Comparative Study of Apriori Algorithms for Parallel Mining of Frequent Itemsets,” International Journal of Computer Applications, 2014.
  13. Tipawan Silwattananusarn1 and Assoc.Prof. Dr. KulthidaTuamsuk2 “Data Mining and Its Applications for Knowledge Management : A Literature Review from 2007 to 2012”, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.2, No.5, September 2012
  14. Han, J., Kamber, M.: “Data Mining Concepts and Techniques”, Morgan Kaufmann Publishers, 2006.
Index Terms

Computer Science
Information Sciences


Association rule Frequent Item Data Mining