Open AccessAnalysis of transaction patterns at drug store with Apriori Algorithm
Author(s) -
Haryo Kusumo,
Dian Marlina,
Mega Novita,
Muchamad Taufiq Anwar
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1869/1/012070
Subject(s) - apriori algorithm , database transaction , transaction data , computer science , data mining , affinity analysis , big data , a priori and a posteriori , order (exchange) , association rule learning , data science , algorithm , database , business , finance , philosophy , epistemology
Data mining is a method for finding hidden data from big data which has been continuously applied in various fields such as marketing, education, bioinformatics and so on. Drug store is one of the business sectors that might take the advantage of the data mining. In the drug store, there is a sales transaction data which contains a big number of data. However, there is a limited number of analysis based on this sales transaction data. There are several information that can be obtained from this big data; one of them is the combination of items that consumers often buy. Apriori Algorithm is a data mining method that has been widely used in order to determine the combinations of frequently purchased products. By using the Apriori Algorithm in the sales transaction data of the drug store.