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Association Rule Development for Market Basket Dataset

by S. S. Bhaskar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 50
Year of Publication: 2018
Authors: S. S. Bhaskar
10.5120/ijca2018917310

S. S. Bhaskar . Association Rule Development for Market Basket Dataset. International Journal of Computer Applications. 180, 50 ( Jun 2018), 12-15. DOI=10.5120/ijca2018917310

@article{ 10.5120/ijca2018917310,
author = { S. S. Bhaskar },
title = { Association Rule Development for Market Basket Dataset },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2018 },
volume = { 180 },
number = { 50 },
month = { Jun },
year = { 2018 },
issn = { 0975-8887 },
pages = { 12-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number50/29576-2018917310/ },
doi = { 10.5120/ijca2018917310 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:04:07.076767+05:30
%A S. S. Bhaskar
%T Association Rule Development for Market Basket Dataset
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 50
%P 12-15
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Term Data mining is used to analyse a big dataset in Statistics. Data mining contains different kinds of approaches like classification, clustering and association. This research work focused on association rule only. association has two special characteristics, which are support and confidence. In this research work, the methodology of association has been studied and developed different rules for a real-life dataset of a super market. These rules are based on three items only.

References
  1. Guoqi Qian, Calyampudi Radhakrishna Rao, Xiaoying Sun, and Yuehua Wu, Boosting association rule mining in large datasets via Gibbs sampling, PubMed.gov, US National Library of Medicine National Institutes of health, volume-113(18):4958-63, (2016).
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  4. Samata Bazar, Phonda, Sindhurdurg, India.
  5. Shalini Bhaskar Bajaj, ARAS: Efficient Generation of Association Rules Using Antecedent Support, 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 978-1-4799-5148-2/14/$31.00, (2014).
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  8. Wiwik Novitasari, Arief Hermawan, Zailani Abdullah, Rahmat Widia Sembiring and Tutut Herawan, A Method of Discovering Interesting Association Rules from Student Admission Dataset, International Journal of Software Engineering and Its Applications Vol. 9, No. 8, 51-66 (2015).
Index Terms

Computer Science
Information Sciences

Keywords

Data mining association Support confidence Lift

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