Circling back to the apriori algorithm, we can use the predicted clusters that were generated instead of lastword, in order to develop some rules:
We will use the coerce to dataframe method to generate the transaction file as previously generated
Create a
rules_clust
object, which builds association rules based upon the itemset of clusters {1,2,3,4,5}Inspect some of the generated rules by lift:
library(arules) colnames(kw_with_cluster2_score) kable(head(kw_with_cluster2_score[,c(1,13)],5)) tmp <- data.frame(kw_with_cluster2_score[,1], kw_with_cluster2_score[,13]) names(tmp) [1] <- "TransactionID" names(tmp) [2] <- "Items" tmp <- unique(tmp) trans4 <- as(split(tmp[,2], tmp[,1]), "transactions") rules_clust <- apriori(trans4,parameter = list(minlen=2,support = 0.02,confidence = 0.01)) summary(rules_clust) tmp <...