Abstract
In this paper, we present a tree-partition algorithm for parallel mining of frequent patterns. Our work is based on FP-Growth algorithm, which is constituted of tree-building stage and mining stage. The main idea is to build only one FP-Tree in the memory, partition it into several independent parts and distribute them to different threads. A heuristic algorithm is devised to balance the workload. Our algorithm can not only alleviate the impact of locks during the tree-building stage, but also avoid the overhead that do great harm to the mining stage. We present the experiments on different kinds of datasets and compare the results with other parallel approaches. The results suggest that our approach has great advantage in efficiency, especially on certain kinds of datasets. As the number of processors increases, our parallel algorithm shows good scalability.
