使用 R 生成所有规则的先验算法
Apriori algorithm to generate all rules using R
这是我在 R
中使用 arules 包生成关联规则的 R 代码
c1 <- c("A","F","D","B")
c2 <- c("D","B","A","C","E")
c3 <- c("C","B","A","E")
c4 <- c("B","D","A")
transactions <- list(c1,c2,c3,c4)
rules_b <- apriori(transactions, parameter = list(supp = 0.6, conf = 0.8, target = "rules"))
结果我只得到 8 条规则。
> inspect(rules_b)
lhs rhs support confidence lift
1 {} => {B} 1.00 1 1
2 {} => {A} 1.00 1 1
3 {D} => {B} 0.75 1 1
4 {D} => {A} 0.75 1 1
5 {B} => {A} 1.00 1 1
6 {A} => {B} 1.00 1 1
7 {B,D} => {A} 0.75 1 1
8 {A,D} => {B} 0.75 1 1
如果我们解决这个问题,我们也可以得到 D => {A,B} 作为规则。但是,它没有显示在这个程序中。我的代码有什么问题?
这个(?apriori
)是否回答了您的问题?
Note: Apriori only creates rules with one item in the RHS
(Consequent)!
这是我在 R
中使用 arules 包生成关联规则的 R 代码c1 <- c("A","F","D","B")
c2 <- c("D","B","A","C","E")
c3 <- c("C","B","A","E")
c4 <- c("B","D","A")
transactions <- list(c1,c2,c3,c4)
rules_b <- apriori(transactions, parameter = list(supp = 0.6, conf = 0.8, target = "rules"))
结果我只得到 8 条规则。
> inspect(rules_b)
lhs rhs support confidence lift
1 {} => {B} 1.00 1 1
2 {} => {A} 1.00 1 1
3 {D} => {B} 0.75 1 1
4 {D} => {A} 0.75 1 1
5 {B} => {A} 1.00 1 1
6 {A} => {B} 1.00 1 1
7 {B,D} => {A} 0.75 1 1
8 {A,D} => {B} 0.75 1 1
如果我们解决这个问题,我们也可以得到 D => {A,B} 作为规则。但是,它没有显示在这个程序中。我的代码有什么问题?
这个(?apriori
)是否回答了您的问题?
Note: Apriori only creates rules with one item in the RHS (Consequent)!