Revenue prediction by mining frequent itemsets with customer analysis

Conventional frequent itemsets mining does not take into consideration the relative benefit or significance of transactions belonging to different customers. Therefore, frequent itemsets with high revenues cannot be discovered through the conventional approach. In this study, we extended the conventional association rule problem by associating the frequency-monetary (FM) weight with a transaction to reflect the interest or intensity of customer values and focusing on revenue. Furthermore, we proposed a new algorithm for discovering frequent itemsets with high revenues from FM-weighted transactions with customer analysis. The experimental results from the survey data revealed that the top k frequent itemsets with high revenues discovered using the proposed approach outperformed those discovered using the conventional approach in the prediction of revenues from customers in next-period transactions.