Frequent pattern mining discovers patterns in transaction databases based only on the relative frequency of occurrence of items without considering their utility.High utility pattern (HUP) mining is one of the most important in data mining due to its ability to consider the non-binary frequency values of items in transactions and different profit values for every item. On the other hand, incremental data mining provide the ability to use previous data structures and mining results in order to reduce unnecessary calculations In this project, three tree structures have been implemented called IHUPL,IHUPTF,IHUPTWU (Incremental High Utility Pattern).All the IHUP tree structures maintains the tf and twu values in the header table and the tree nodes. The first tree structure is arranged in the lexicographic order(IHUPL). It can capture the incremental data without any restructuring operation. The second tree structure is arranged in descending order based on the transaction frequency(IHUPTF).The third tree structure is arranged in the descending order based on the transaction weighted utilization(IHUPTWU) to reduce the mining time. IHUP has the "build once mine property" and is suitable for incremental mining. The experimental results shows that the tree structures are efficient and scalable for incremental mining.