Two top-k HUIM algorithms based on the particle filter theory
摘要
Top-k high utility itemset mining(Top-k HUIM) has emerged as a critical research area, facilitating the discovery of valuable itemsets without predefined thresholds. Existing methods primarily focus on datasets without negative utilities, while approaches for handling negative utilities remain limited. Additionally, many top-k HUIM techniques require multiple global scans and large data structures, which hinder their efficiency and scalability. To address these challenges, we propose two novel algorithms: PFH (Particle Filter-based top-k HUIM for datasets without negative utilities) and PFHN (Particle Filter-based top-k HUIM for datasets with Negative utilities). PFH introduces a novel transmission process by assigning transition probabilities to particles for updating their states. A criterion for particle degeneration is proposed to terminate the transmission process, and a resampling strategy is employed to mitigate particle degeneration and improve algorithmic efficiency. In order to handle datasets with negative utilities, PFHN further introduces a utility flag filtering mechanism and employs a pruning strategy distinct from PFH to enhance efficiency. Extensive experiments demonstrate that both PFH and PFHN can efficiently and accurately mine top-k HUIs, providing a novel perspective for solving the top-k HUIM problem.