【摘要】 关联规则挖掘可以发现大量数据项集之间隐含的关系,在许多领域得到了广泛应用。目前很多关联规则挖掘算法已经被提出,这些算法一般都认为每个数据项的重要性相同。然而在现实中各个项目的重要性往往不同,从决策者角度出发,他们往往会优先考虑利润较高的项目,而忽略利润较低的项目。论文分析了现有加权关联规则文献中存在的问题,提出了一种新的加权关联规则模型,给出了有效挖掘加权频繁项集的MWFI算法。更多还原

【Abstract】 Mining association rules can find out some potential correlations in large quantity of data and has been applied widely in some fields.Lots of algorithms have been proposed for finding the association rules at present.Most of them treat each item uniformly.However,in real applications,the importance of items is different.Decision-makers are more inclined to items whose profits are higher than others.The shortages of the existing algorithms for mining weighted association rules in some other papers are analyzed,at the same time,a new weighted association rules model and an effective algorithm MWFI to handle the problem of mining weighted frequent itemsets are proposed in this paper.更多还原