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Reseach Article

Association Rule Mining using Improved Apriori Algorithm

by Minal G. Ingle, N. Y. Suryavanshi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 112 - Number 4
Year of Publication: 2015
Authors: Minal G. Ingle, N. Y. Suryavanshi

Minal G. Ingle, N. Y. Suryavanshi . Association Rule Mining using Improved Apriori Algorithm. International Journal of Computer Applications. 112, 4 ( February 2015), 37-42. DOI=10.5120/19658-1297

@article{ 10.5120/19658-1297,
author = { Minal G. Ingle, N. Y. Suryavanshi },
title = { Association Rule Mining using Improved Apriori Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 112 },
number = { 4 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 37-42 },
numpages = {9},
url = { },
doi = { 10.5120/19658-1297 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:48:36.079193+05:30
%A Minal G. Ingle
%A N. Y. Suryavanshi
%T Association Rule Mining using Improved Apriori Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 112
%N 4
%P 37-42
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

The past few years have seen a marvellous curiosity in area of data mining. Data mining is usually thought of as the process of finding hidden, non trivial and formerly unknown information in large collection of data. Association rule mining is an significant component of data mining. Association rule are an important class of methods of finding regularities or patterns in data. Association rule mining has been used in several application domains. The ideal application of Association Rule Mining is market basket analysis. Apriori algorithm generates interesting frequent or infrequent candidate item sets with respect to support count. Apriori algorithm can require to produce vast number of candidate sets. To generate the candidate sets, it needs several scans over the database. Apriori acquires more memory space for candidate generation process. While it takes multiple scans, it must require a lot of I/O load. The approach to overcome the difficulties is to get better Apriori algorithm by making some improvements in it. Also will develop pruning strategy as it will decrease the scans required to generate candidate item sets and accordingly find a valence or weightage to strong association rule. So that, memory and time needed to generate candidate item sets in Apriori will reduce. And the Apriori algorithm will get more effective and efficient.

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Index Terms

Computer Science
Information Sciences


Data Mining Association Rule Mining Apriori algorithm Frequent Itemsets Association Rules