A Subset-Lattice Algorithm for Mining High Utility Patterns over the Data Stream Sliding Window

High utility pattern mining considers each item with a distinct profit or price. The problem is that infrequent patterns may contribute a great number of profit, whereas frequent patterns may only contribute a small amount of profit. The SHU-Grow algorithm uses the tree-based data structure to mine high utility patterns. In such a structure, the SHU-Grow algorithm always records the estimated value of each pattern. Then, such an algorithm has to identify actual high utility patterns from the candidate patterns. In this paper, we propose the Subset-Lattice algorithm based on the sliding window model. Our algorithm utilizes the lattice structure to record the information of the transactions and to store relationship between the child node and the parent node. From the performance study, we show that our Subset-Lattice algorithm could provide better performance than the SHU-Grow algorithm both in the processing time and storage space.