High utility itemset mining (HUIM) is one of the most compelling problems in data mining, extending frequent itemset mining (FIM) and serving as a crucial method for analyzing customer behavior. Many HUIM algorithms have been proposed to improve execution time and memory consumption. However, most assume that the profit is fixed for each item in a database, which is unrealistic. Some algorithms address products with unstable transaction profits but still need to run faster due to ineffective pruning strategies. Additionally, generalizing items into categories is often neglected. To address these issues, this paper considers a more practical database type that integrates unstable profits with a taxonomy of items. The proposed algorithm, CLHUN (Cross-level High Utility Itemset Mining in a Database with Unstable and Negative Profits), combines efficient techniques such as item sorting and tighter upper bounds to prune the search space. Furthermore, it introduces strategies to eliminate unpromising items during mining and reduce the number of transaction scans. Several experiments were conducted to evaluate the algorithm's performance. Results demonstrate that CLHUN is efficient with these techniques and strategies.