如何提取最长的先验规则（关联规则）

Question

当使用下面的例子时：

library("arules")
data("Adult")
## Mine association rules.
rules <- apriori(Adult,parameter = list(supp = 0.5, conf = 0.9, target = "rules"))
> labels(rules)

您将看到以下规则：

[5] "{sex=Male} => {capital-gain=None}"  
[20] "{race=White,sex=Male} => {capital-gain=None}"
[22] "{sex=Male,native-country=United-States} => {capital-gain=None}"

具有相同的 RHS，但 LHS 不同。我只想获得最长的 LHS 规则并忽略较短的规则。在上面提到的示例中，我想省略规则 [5]，因为它包含在 [20] 和 [22] 中。（{sex=Male} 包含在 [20] 和 [22] 中）。我只想保留最长的规则（在其他示例中，最长的规则可以包含 3 个或更多组件）。

Answer 1

使用is.subset得到一个逻辑矩阵，并使用该矩阵定位非子集：

subsets <- is.subset(rules, proper = TRUE)
subsets[lower.tri(subsets, diag=TRUE)] <- 0 # set lower triangle to 0
notsubsets <- rowSums(subsets) == 0L
labels(rules[notsubsets])


# [1] "{capital-gain=None,hours-per-week=Full-time} => {capital-loss=None}"                      
# [2] "{capital-loss=None,hours-per-week=Full-time} => {capital-gain=None}"                      
# [3] "{race=White,sex=Male} => {capital-gain=None}"                                             
# [4] "{race=White,sex=Male,native-country=United-States} => {capital-loss=None}"                
# [5] "{race=White,sex=Male,capital-loss=None} => {native-country=United-States}"                
# [6] "{sex=Male,capital-loss=None,native-country=United-States} => {race=White}"                
# [7] "{sex=Male,capital-gain=None,native-country=United-States} => {capital-loss=None}"         
# [8] "{workclass=Private,race=White,native-country=United-States} => {capital-loss=None}"       
# [9] "{workclass=Private,race=White,capital-loss=None} => {native-country=United-States}"       
#[10] "{workclass=Private,race=White,capital-gain=None} => {capital-loss=None}"                  
#[11] "{workclass=Private,race=White,capital-loss=None} => {capital-gain=None}"                  
#[12] "{workclass=Private,capital-gain=None,native-country=United-States} => {capital-loss=None}"
#[13] "{workclass=Private,capital-loss=None,native-country=United-States} => {capital-gain=None}"
#[14] "{race=White,capital-gain=None,native-country=United-States} => {capital-loss=None}"       
#[15] "{race=White,capital-loss=None,native-country=United-States} => {capital-gain=None}"       
#[16] "{race=White,capital-gain=None,capital-loss=None} => {native-country=United-States}"

is.subset 在评估它是否重复时计算右侧，这是这种方法的问题。如评论中所述，上述方法错过了规则 {sex=Male,native-country=United-States} => {capital-gain=None}:

labels(rules[c(22, 43)])
#[1] "{sex=Male,native-country=United-States} => {capital-gain=None}"                  
#[2] "{sex=Male,capital-gain=None,native-country=United-States} => {capital-loss=None}"
is.subset(rules[22], rules[43])

要获得这些情况，您可以使用 <= 1L 而不是 == 0L，但是您也会得到误报（"{sex=Male,capital-gain=None} => {capital-loss=None}" 是 [=18 的子集=].

如何提取最长的先验规则（关联规则）

How to extract the longest apriori rule (association rule)

r

labels

apriori