This paper deals with the task of mining high utility itemsets. The proposed approach presents a unified framework for efficiently mining high utility patterns from transaction databases while handling effectively various condensed representations. In addition, this approach offers a way to integrate multiple constraints, including closedness, minimality, and maximality, while maintaining flexibility in the mining process. This allows to significantly enhance the efficiency and effectiveness of mining high utility patterns, making it a valuable tool for various data mining applications. Finally, we show through an extensive campaign of experiments on several popular real-life datasets the efficiency of our proposed approach.