挖掘意外高效用项集的有效方法

An Efficientive Method For Mining Unexpected High Utility Itemsets

针对传统的高效用项集挖掘存在可能会丢失特定切片上的意外项集的利润、缺乏反单调性、计算量大等问题,提出了挖掘意外高效用项集(Unexpected High Utility Itemsets Mining, UHUIM)的算法。算法用于挖掘意外的高效用项集,给需要定期分析的数据集带来了意外的利润。上述算法提出了意外高效用列表的数据结构(UHUI-list),能够更加紧凑的存储项集的有用信息且在挖掘过程中重用内存,提高了挖掘效率及节省存储空间;所提算法提出了UHUI-Prune策略,有效地缩小了挖掘过程中的搜索空间。在3个真实数据集上进行性能评估,上述算法在运行时间、存储空间及可伸缩性方面皆优于ULB-Miner算法及HUI-Miner算法。

Aiming at the problems of traditional efficient itemset mining,such as the loss of profits of unexpected itemsets on specific slices,the lack of anti-monotonicity,and the large amount of computation,this paper proposes an algorithm for mining unexpected high utility itemsets mining(UHUIM).The algorithm was used to mine unexpected high utility item sets,bringing unexpected profits to the datasets that need to be analyzed regularly.The above algorithm proposes the data structure of unexpected high utility list,which can store the useful information of itemsets more compactly and reuse the memory during the mining process as well as improve the mining efficiency and saving the storage space.It also proposes the UHUI-Prune strategy,which can effectively reduce the search space during the mining process.The algorithm outperforms the ULB-Miner and HUI-Miner algorithms in terms of running time,storage space and scalability when evaluated on three real datasets.