Weighted frequent pattern (WFP) mining is considered to be more effective than traditional frequent pattern mining because of its consideration of different semantic significance (weights) of items. However, most existing WFP algorithms assume a static weight for each item, which may not be realistically hold in many real-life applications. In this paper, we consider the concept of a dynamic weight for each item and address the situations where the weights of an item can be changed dynamically. We propose a novel tree structure called compact pattern tree for dynamic weights (CPTDW) to mine frequent patterns from dynamic weighted item containing databases. The CPTDW-tree leads to the concept of dynamic tree restructuring to produce a frequency-descending tree structure at runtime. CPTDW also ensures that no non-candidate item can appear before candidate items in any branch of the tree, and thus speeds up the construction time for prefix tree and its conditional tree during the mining process. Furthermore, as it requires only one database scan, it can be applicable to interactive, incremental, and/or stream data mining. Evaluation results show that our proposed tree structure and the mining algorithm outperforms previous methods for dynamic weighted frequent pattern mining.