Information about consumer spending patterns is needed by retail companies in determining promotion strategies, placing merchandise on shelves, and inventory management. Most applications in retail companies only produce information from the company side. Applications owned by the company have not been able to generate transaction activity information from the customer side. The transaction activity information from the customer side is knowing consumer spending patterns in terms of buying goods. This information can overcome problems that are often faced by companies, namely customer complaints when there is a vacancy in inventory of pairs of goods that are often purchased by customers. This article describes the application of association data mining with the Apriori TID algorithm. Apriori-TID is a development of the Apriori algorithm. Apriori-TID plays a role in finding frequent itemsets based on minimum support. The application can generate goods association rules based on the minimum support and confidence values entered by the user. The association data mining application features dataset import, dataset analysis, and print analysis result reports. The association rules generated by the application are evaluated using the lift ratio method. The number of transactions, minimum support, and minimum confidence affect the number of rules generated by the application.