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Reseach Article

Frequent Item Set Mining using Association Rules

by Amritpal Kaur, Vaishali Aggarwal
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 3 - Number 5
Year of Publication: 2015
Authors: Amritpal Kaur, Vaishali Aggarwal

Amritpal Kaur, Vaishali Aggarwal . Frequent Item Set Mining using Association Rules. Communications on Applied Electronics. 3, 5 ( November 2015), 18-20. DOI=10.5120/cae2015651945

@article{ 10.5120/cae2015651945,
author = { Amritpal Kaur, Vaishali Aggarwal },
title = { Frequent Item Set Mining using Association Rules },
journal = { Communications on Applied Electronics },
issue_date = { November 2015 },
volume = { 3 },
number = { 5 },
month = { November },
year = { 2015 },
issn = { 2394-4714 },
pages = { 18-20 },
numpages = {9},
url = { http://localhost:9000/archives/volume3/number5/462-2015651945/ },
doi = { 10.5120/cae2015651945 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-09-04T19:44:01.225414+05:30
%A Amritpal Kaur
%A Vaishali Aggarwal
%T Frequent Item Set Mining using Association Rules
%J Communications on Applied Electronics
%@ 2394-4714
%V 3
%N 5
%P 18-20
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

One of the most difficult tasks in data mining is to fetch the frequent item set from large database. Related to this many conquering algorithms have been introduced till now. Whereas frequent item set figures out pattern, correlation as well as association between items in a bulky database and these constraints provides better scope in mining process. During study it has been founded that either support count or candidate count are been taken into consideration by using less and strong association rules. But this approach doesn’t improve time parameter (which seems to be constant). Our proposed work is based on reducing time component by considering both pre and post processing results in each transaction. As a result frequent item set will be formed by applying further strong association rules with the help of pre defined support and candidate count. In result this approach will acquire better performance.

  1. Basheer Mohamad Al-Maqaleh, S. K. (2013). An Efficient Algorithm for Mining Association Rules using Confident Frequent Itemsets.
  2. Cesario Eugenio, Carlo Mastroianni, Domenico Talia. (2014). A Multi-Domain Architecture for Mining Frequent Items and Itemsets from Distributed Data Streams.
  3. Eugenio cesario, C. M. (2013). A Multi Domain Architecture for Mining Frequent Items and Itemsets srom Distributed Data Streams.
  4. M. Jeyasutha, D. F. (2015). Closed Frequent Itemsets mining over Data streams for Visualizing Network Traffic.
  5. Shaobo Shi, Y. Q. (2013). Accelerating Intersection Computation in Frequent Itemset Mining with FPGA.
  6. Suhasini A. Itkar, U. V. (2013). Distributed Algorithm for Frequent Pattern Mininh using HadoopMap Reduce Framework.
  7. Sujatha Dandu, B. P. (2013). Improved Algorithm for Frequent item sets Mining based on Apriori and FP-tree.
  8. XuePing Zhang, Y. Z. (2010). Improved Parallel Algorithm for Mining Frequent Item-set Used in HRM.
  9. Yen-hui Liang, S.-y. W. (2015). Sequence -Growth : A Scalable and Effective Frequent Itemset Mining Algorithm for Big Data Based on MapReduce Framework.
Index Terms

Computer Science
Information Sciences


Data mining; pre-processing; post-processing; confidence count; support count; association rules;