计算R中关联规则的信念值？

Question

我在R中使用apriori算法挖掘关联规则。我可以根据信心，提升和支持来检查结果规则，但希望评估每条规则的定罪值。有人知道如何在R中做到这一点吗？

Answer 1

看看?arules::interestMeasure。例如，在Wikipedia example之后，您可以执行以下操作：

df <- read.table(header=T, text="ID     milk    bread   butter  beer    diapers
1   T   T   F   F   F
2   F   F   T   F   F
3   F   F   F   T   T
4   T   T   T   F   F
5   F   T   F   F   F")
library(arules)
trans <- as(df[, -1], "transactions")
rules <- apriori(trans, list(supp = 0.01, conf = 0.01, minlen = 2))
cbind(as(rules, "data.frame"), conviction=interestMeasure(rules, "conviction", trans))
#                       rules support confidence      lift conviction
# 1       {beer} => {diapers}     0.2  1.0000000 5.0000000         NA
# 2       {diapers} => {beer}     0.2  1.0000000 5.0000000         NA
# 3        {butter} => {milk}     0.2  0.5000000 1.2500000        1.2
# 4        {milk} => {butter}     0.2  0.5000000 1.2500000        1.2
# 5       {butter} => {bread}     0.2  0.5000000 0.8333333        0.8
# 6       {bread} => {butter}     0.2  0.3333333 0.8333333        0.9
# 7         {milk} => {bread}     0.4  1.0000000 1.6666667         NA
# 8         {bread} => {milk}     0.4  0.6666667 1.6666667        1.8
# 9  {milk,butter} => {bread}     0.2  1.0000000 1.6666667         NA
# 10 {bread,butter} => {milk}     0.2  1.0000000 2.5000000         NA
# 11 {milk,bread} => {butter}     0.2  0.5000000 1.2500000        1.2