Application of Apriori Algorithm to Find Flower Purchase Patterns

Abstract

This research aims to apply the Apriori algorithm in analyzing flower
purchase patterns at a flower shop. Apriori algorithm is used to identify
product combinations that are often purchased together, in the hope of finding
purchasing patterns that can be utilized to improve marketing strategies and
store operational efficiency. Transaction data from the shop is processed to
extract frequent itemsets and generate association rules by setting the right
threshold of support and confidence values. The results of this study show that
flower combinations such as Tulip and Bougenville frequently co-occur in
purchases, with significant support-confidence products. These findings
provide insights into consumer purchasing behavior that can be used to
recommend product bundling or product rearrangement in stores. This
research contributes to the application of data mining in the retail sector,
particularly in increasing sales and customer satisfaction in flower shops.