Analysis of Puchase Patterns on Office Stationery Sales Data using Apriori Algorithm

Abstract

This study analyzes purchasing patterns in office stationery sales using the
Apriori algorithm, a data mining method for generating association rules and
frequent itemsets. The research examines transaction data to identify
combinations of frequently purchased items, aiming to improve inventory
management and marketing strategies. The Apriori algorithm calculates
metrics such as support, confidence, and lift to determine strong associations
between items. Results indicate key purchasing patterns, such as frequent co
purchases of notebooks and pencils, which inform targeted promotions and
stock planning. The findings highlight the potential of data-driven decision
making to enhance business efficiency and customer satisfaction in the retail
sector.