关于数据挖掘中的apriori算法,帮忙推出关联规则 事务数为 5 支持度为0.6,置信度为0.6

2024-12-02 13:54:21
推荐回答(2个)
回答1:

abc的支持数P1=3,acd的支持数P2=3,bcd的支持数P3=3,关联规则的输出就是在由频繁项集的项组成的关联规则中,找出置信度大于等于最小置信度阈值的关联规则。因为由频繁项集的项组成的关联规则的支持度大于等于最小支持阈值,所以规则产生过程就是在由频繁项集的项组成的关联规则中,找出置信度大于等于最小置信度阈值的强关联规则,基本步骤如下:
1) 对于每个频繁项集L,产生L的所有非空真子集。
2) 对于L的每个非空真子集 ,如果L的支持计数除以 的支持计数大于等于最小置信度阈值min_conf,则输出强关联规则 =>(L- )。
例如:abc的非空真子集有a,b,c,ab,ac,bc。分别算出他们的支持数,再除以abc的支持数,若结果大于0.6则可输出强关联规则。Pa/P1=4/3>0.6,则可以输出关联规则:a=>bc;同理可输出强关联规则:b=>ac,c=>ab;
同理,对于acd,bcd采用同样地方法。

回答2:

对于每个频繁项集X,有非空子集Y,如果support(X)/support(Y)>=0.6(置信度阈值),那么输出规则X->(X-Y)
给你举个例子:
abc,有非空子集ac,support(acb)/support(ac)=3/4=0.75>=0.6,那么输出规则abc->b
建议你写程序做吧。
以上是我的理解,希望对你有用~

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