I am using the apriori algorithm in R to mine for association rules. I can inspect the resulting rules based on confidence, lift, and support, but was hoping to evaluate the conviction values for each rule. Does anyone know how to do this in R?
Have a look at ?arules::interestMeasure. For instance, following the Wikipedia example, you can do:
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