Association Rules Mining on Retail Data

Hatice Dağaslanı () and Özlem Deniz Başar ()
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Hatice Dağaslanı: İstanbul Commerce University, Natural and Applied Sciences, Department of Statistics, Istanbul, Turkiye.
Özlem Deniz Başar: İstanbul Commerce University, Graduate School of Natural and Applied Sciences, Department of Statistics, İstanbul, Türkiye.

EKOIST Journal of Econometrics and Statistics, 2022, vol. 0, issue 37, 199-211

Abstract: The development in information technologies, artificial intelligence, and data mining benefits people in many areas. With this development, data stacks are formed through the storage of ever-increasing data. Accessing useful information from the data heaps is a very difficult process. This has led to the emergence and development of the concept of data mining. In this study, the relationship between the categories of the products sold by a company in the retail sector operating in Turkey was analyzed using the Apriori algorithm, which is an algorithm used in data mining. In the application, one-day sales data of the company was used. The data obtained was provided to extract the association rules with the help of Python. In this way, the purchasing habits of customers were determined by finding meaningful relationships between products using association rules.

Keywords: Apriori Algorithm; Association Rules Analysis; Data Mining (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:ist:ekoist:v:0:y:2022:i:37:p:199-211

DOI: 10.26650/ekoist.2022.37.1145052

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