Open AccessApplication of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom
Author(s) -
Mardi Santoso
Publication year - 2021
Publication title -
brilliance
Language(s) - English
Resource type - Journals
ISSN - 2807-9035
DOI - 10.47709/brilliance.v1i2.1228
Subject(s) - association rule learning , apriori algorithm , a priori and a posteriori , computer science , data mining , database transaction , transaction data , purchasing , value (mathematics) , association (psychology) , affinity analysis , algorithm , machine learning , database , philosophy , operations management , epistemology , economics
Data mining can generally be defined as a technique for finding patterns (extraction) or interesting information in large amounts of data that have meaning for decision support. One of the well-known and commonly used association rule discovery data mining methods is the Apriori algorithm. The Association Rule and the Apriori Algorithm are two very prominent algorithms for finding a number of frequently occurring sets of items from transaction data stored in databases. The calculation is done to determine the minimum value of support and minimum confidence that will produce the association rule. The association rule is used to produce the percentage of purchasing activity for an itemset within a certain period of time using the RapidMiner software. The results of the test using the priori algorithm method show that the association rule, that customers often buy toothpaste and detergents that have met the minimum confidence value. By searching for patterns using this a priori algorithm, it is hoped that the resulting information can improve further sales strategies.