数据流上的最大频繁项集挖掘方法

摘要

最大频繁项集适用于内存空间有限的数据流挖掘.为此,提出一种基于界碑模型的最大频繁项集挖掘方法,采用最大频繁项集树的数据结构,增量式地维护最大频繁项集与部分附属信息,实现项集的快速搜索和裁剪.在MUSHROOM和BMS-POS数据集上的实验结果表明,该方法具有较高的挖掘效率.%Maximal frequent itemsets is suitable for stream mining, which store most of the information contained in frequent itemsets using less space. This paper focuses on mining maximal frequent itemsets incrementally over streams under landmark model. It designs a simple and compacted data structure to effectively maintain a dynamically selected set of itemsets for quickly node search and pruning. Experimental results on the MUSHROOM and BMS-POS datasets show that this method has higher mining efficiency.