Data mining market basket analysis' using hybrid-dimension association rules, case study in Minimarket X

Market-Basket Analysis is a process to analyze the habits of buyers to find the relationship between different items in their market basket. The discovery of these relationships can help the merchant to develop a sales strategy by considering the items frequently purchased together by customers. In this research, the data mining with market basket analysis method is implemented, where it can analyze the buying habit of the customers. The testing is conducted in Minimarket X. Searching for frequent itemsets performed by Apriori algorithm to get the items that often appear in the database and the pair of items in one transaction. Pair of items that exceed the minimum support will be included into the frequent itemsets are selected. Frequent itemsets that exceed the minimum support will generate association rules after decoding. One frequent itemsets can generate association rules and find the confidence, which is uses a hybrid-dimension association rules. The test results show, the application can generate the information what kind of products are frequently bought in the same time by the customers according to Hybrid-dimension Association Rules criteria. Results from the mining process show a correlation between the data (association rules) including the support and confidence that can be analyzed. This information will give additional consideration for owners of Minimarket X to make the further decision.