动态数据库中增量Top-k高效用模式挖掘算法

摘要

Setting an appropriate threshold was essential to high utility pattern mining,however it was a difficult task for users.If the threshold was too low,a large number of low utility patterns were generated;if the threshold was too high,no high utility patterns might be generated.According,Top-k high utility pattern mining was proposed,in which k was the number of highest utility patterns.Most studies on high utility mining were only designed for static database,yet in real-world applications some new transactions inserted in original database.To address the above issues,this paper proposed an incremental Top-k high utility pattern mining algorithm,named TOPK-HUP-INS.The algorithm adopted four effective strategies to mine the high utility patterns that the number was user-specified in the case of incremental data.Comparing the experimental results on different datasets show that TOPK-HUP-INS performs well in terms of time and space.%高效用模式的挖掘需要设定一个合适的阈值,而阈值设定对用户来说并非易事,阈值过小导致产生大量低效用模式,阈值过大可能导致无高效用模式生成.因而Top-k高效用模式挖掘方法被提出,k指效用值前k大的模式.并且大量的高效用挖掘研究仅针对静态数据库,但在实际应用中常常会遇到新事务的加入的情况.针对以上问题,提出了增量的Top-k高效用挖掘算法TOPK-HUP-INS.算法通过四个有效的策略,在增量数据的情况下,有效地挖掘用户所需数量的高效用模式.通过在不同数据集上的对比实验表明TOPK-HUP-INS算法在时空性能上表现优异.