基于时效性和相关性约束的周期模式挖掘

Periodic pattern mining based on recency and correlation constraints

传统周期模式挖掘忽略了模式本身的相关性和时效性,导致获取到一些实用价值有限的弱相关且时效性较低的模式。因此,提出了新颖的基于时效性和相关性约束的周期模式挖掘方法(correlation and recency periodic frequent pattern-breadth first search,CRPFP-BFS)和(correlation and recency periodic frequent pattern-depth first search,CRPFP-DFS)。将给定的数据库压缩到一个列式结构的列表CRPFP-List中,CRPFP-BFS和CRPFP-DFS分别采用广度优先和深度优先搜索方式递归地进行挖掘,同时利用支持度、周期、时效性以及相关性剪枝策略减少搜索空间,以有效地发现相关时效周期模式。与当前最先进算法在密集数据集和稀疏数据集上进行对比实验,结果表明CRPFP-BFS和CRPFP-DFS具有较低的内存占用和更高的运行效率,并且具有良好的可扩展性,其中CRPFP-DFS适合于内存要求严格的情况,CRPFP-BFS在长事务稀疏数据集下的运行效率更高。

Traditional periodic pattern mining ignoreds the correlation and recency of the patterns,and therefore obtains some weakly correlated and recency patterns with limited practical value.To address the issue,this paper proposed novel periodic pattern mining methods based on recency and correlation constraints named CRPFP-BFS and CRPFP-DFS.By transforming a given database into a column-based structure CRPFP-List,CRPFP-BFS and CRPFP-DFS recursively mined its data using breadth-first and depth-first search,respectively.At the same time,CRPFP-BFS and CRPFP-DFS applied four pruning strategies based on support,period,recency and correlation to reduce the search space,thereby effectively discovering correlation recency periodic patterns.By comparing with the current state-of-the-art algorithms on dense and sparse datasets,and the experimental results show that the CRPFP-BFS and CRPFP-DFS have lower memory usage,higher operating efficiencies and good scalability.Among them,CRPFP-DFS is suitable for situations with strict memory requirements,and CRPFP-BFS performs more efficiently for long transaction sparse databases.