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Association Rule Mining With ECLAT on a Malaysian Retail Store

Ankita Nandy

Department of Computing Asia Pacific University Kuala Lumpur, Malaysia

31-49

Vol: 8, Issue: 1, 2018

Receiving Date: 2017-12-02 Acceptance Date:

2018-01-17

Publication Date:

2018-01-29

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Abstract

Association rule mining is a popular knowledge discovery algorithm in the retail industry. The knowledge is obtained in the form of rules. As what people buy is driven by reason and not entirely random, analysing their purchases gives an insight into their social, cultural and economic preferences. This understanding of the customer behaviour gives the store management a competitive edge, higher revenue and increased customer loyalty. This paper presents an implementation on the transactional dataset from a retail store in Malaysia. The weighted Eclat algorithm in R ‘arules’ package has been used to obtain the association rules for this dataset. A set of 110 rules with high lifts have been obtained which have been analysed to formulate solutions to optimize store layout, suggest cross selling opportunities, boost sales and customer satisfaction.

Keywords: ECLAT; retail; Malaysia; association rule mining

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