Running the apriori algorithm on the clusters

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 &lt...