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Reseach Article

A Novel Association Rule Algorithm to Discover Maximal Frequent Item Set

by Hartej Singh, Vinay Dwivedi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 137 - Number 9
Year of Publication: 2016
Authors: Hartej Singh, Vinay Dwivedi
10.5120/ijca2016908883

Hartej Singh, Vinay Dwivedi . A Novel Association Rule Algorithm to Discover Maximal Frequent Item Set. International Journal of Computer Applications. 137, 9 ( March 2016), 1-4. DOI=10.5120/ijca2016908883

@article{ 10.5120/ijca2016908883,
author = { Hartej Singh, Vinay Dwivedi },
title = { A Novel Association Rule Algorithm to Discover Maximal Frequent Item Set },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 137 },
number = { 9 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume137/number9/24300-2016908883/ },
doi = { 10.5120/ijca2016908883 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:37:53.053016+05:30
%A Hartej Singh
%A Vinay Dwivedi
%T A Novel Association Rule Algorithm to Discover Maximal Frequent Item Set
%J International Journal of Computer Applications
%@ 0975-8887
%V 137
%N 9
%P 1-4
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Association Rule mining is a sub-discipline of data mining. Apriori algorithm is one of the most popular association rule mining technique. Apriori technique has a disadvantage that before generating a maximal frequent set it generates all possible proper subsets of maximal set. Therefore it is very slow as it requires many database scans before generating a maximal frequent itemset In the method proposed in this paper entire database is scanned only once. Frequency count of all distinct transactions is stored in a hash map. Algorithm maintains an array of tables such that each table in the array contain frequency count of all potential k-itemsets..Binary search and the concept of longest common subsequence are used to efficiently extract maximal frequent itemset. Experimental results show that proposed algorithm performs better than apriori algorithm.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Association rule mining Apriori algorithm Frequent itemset Hashing Longest common subsequence