Fast mining Top-Rank-k frequent patterns by using Node-lists

• In this paper, we propose an efficient algorithm named NTK for mining Top-Rank-k frequent patterns.
• NTK employs Node-lists to represent patterns, implement the recommendation task.
• Experiment results on four real datasets show that NTK far outperforms state-of-the-art algorithms.

Mining Top-Rank-k frequent patterns is an emerging topic in frequent pattern mining in recent years. In this paper, we propose a new mining algorithm, NTK, to mining Top-Rank-k frequent patterns. The NTK algorithm employs a data structure, Node-list, to represent patterns. The Node-list structure makes the mining process much efficient. We have experimentally evaluated our algorithm against two representative algorithms on four real datasets. The experimental results show that the NTK algorithm is efficient and is at least two orders of magnitude faster than the FAE algorithm and also remarkably faster than the VTK algorithm, the recently reported state-of-the-art algorithm for mining Top-Rank-k frequent patterns.