基于时序和兴趣度约束的加权关联规则挖掘算法研究

摘要

In order to solve the problems that the frequent set information mining is not perfect in the association rule mining algorithm and the timing cycle influences mining of transaction set frequent item, a weighted association rules mining algorithms based on the timing and interest degrees constraints was proposed. This algorithm firstly uses sequential sliding function of the timing affairs set for probability estimation and weight assignment,then sets simplification according to the interests of the constraint function and pruning theorem transaction, makes a weighted frequent transaction set extraction according to the degree of support and support expectations, lastly derives weighted association rules based on the confidence. The experimental results show that the algorithm can meet the users requirement in mining association rules quickly and effectively.%为了解决关联规则挖掘算法中频繁集信息挖掘不完善和时序周期对事务集频繁项挖掘的影响问题,提出了一种基于时序和兴趣度约束的加权关系规则挖掘算法.该算法首先利用时序滑动函数对时序事务集进行发生概率估算和权值赋值,依据兴趣度约束函数和剪枝定理进行事务集化简,然后根据支持度和k支持期望进行加权频繁事务集抽取,最后依据置信度进行加权关联规则导出.实验结果证明,该算法能够快速有效地挖掘出符合用户兴趣度的关联规则.