关键词: 数据挖掘, top-k, 频繁模式, 节点集

Abstract: The number of mined patterns is usually too large and a small number of frequent patterns are used in real application. Therefore, the mining of top-rank-k frequent patterns which limits the number of mined frequent patterns by ranking them in frequency, has improved the efficiency of the algorithm. This paper proposes the TPN algorithm for mining top-k frequent patterns. The TPN employs a new data structure, Nodesets, to represent patterns, compressing the data to Poc-tree and computing min support patterns to limit candidate items by the top-k-rank table. The experiments are conducted to evaluate TPN and ATFP, Top-k-FP-growth in terms of mining time for two datasets. The experimental results show that TPN is more efficient and faster.

Key words: data mining, top-k, frequent patterns, nodesets