机器算法验证 - 使用 arules 为新数据寻找合适的规则 - 吾爱随笔录

我正在使用 R（和 arules 包）来挖掘关联规则的交易。我想做的是构建规则，然后将它们应用于新数据。

例如，假设我有很多规则，其中之一是规范的{Beer=YES} -> {Diapers=YES}.

然后我有新的交易数据，其中一条记录购买了啤酒但没有购买尿布。如何识别满足 LHS 但尚未满足 RHS 的规则？

示例：

install.packages("arules")
library(arules)

data("Groceries")
**#generate Rules omitting second record**

rules <- apriori(Groceries[-2],parameter = list(supp = 0.05, conf = 0.2,target = "rules"))

生成的规则是：

> inspect(rules)
  lhs                   rhs                   support confidence     lift
1 {}                 => {whole milk}       0.25554200  0.2555420 1.000000
2 {yogurt}           => {whole milk}       0.05603010  0.4018964 1.572722
3 {whole milk}       => {yogurt}           0.05603010  0.2192598 1.572722
4 {rolls/buns}       => {whole milk}       0.05664023  0.3079049 1.204909
5 {whole milk}       => {rolls/buns}       0.05664023  0.2216474 1.204909
6 {other vegetables} => {whole milk}       0.07484238  0.3867578 1.513480
7 {whole milk}       => {other vegetables} 0.07484238  0.2928770 1.513480

第二笔交易显示该客户，因为他们有酸奶但没有全脂牛奶，可能应该发送牛奶优惠券。如何为新交易找到“规则”中的任何适用规则？

> LIST(Groceries[2])
[[1]]
[1] "tropical fruit" "yogurt"         "coffee"

basket <- Groceries[2] # find all rules, where the lhs is a subset of the current basket rulesMatchLHS <- is.subset(rules@lhs,basket) # and the rhs is NOT a subset of the current basket (so that some items are left as potential recommendation) suitableRules <- rulesMatchLHS & !(is.subset(rules@rhs,basket)) # here they are inspect(rules[suitableRules]) # now extract the matching rhs ... recommendations <- strsplit(LIST(rules[suitableRules]@rhs)[[1]],split=" ") recommendations <- lapply(recommendations,function(x){paste(x,collapse=" ")}) recommendations <- as.character(recommendations) # ... and remove all items which are already in the basket recommendations <- recommendations[!sapply(recommendations,function(x){basket %in% x})] print(recommendations)

> inspect(rules[suitableRules]) lhs rhs support confidence lift 1 {} => {whole milk} 0.2555420 0.2555420 1.000000 2 {yogurt} => {whole milk} 0.0560301 0.4018964 1.572722 > print(recommendations) [1] "whole milk"