K-means clustering of terms

Now we can cluster the term document matrix using k-means. For illustration purposes, we will specify that five clusters be generated:

kmeans5 <- kmeans(dtms, 5)

Once k-means is done, we will append the cluster number to the original data, and then create five subsets based upon the cluster:

kw_with_cluster <- as.data.frame(cbind(OnlineRetail, Cluster = kmeans5$cluster))

 # subset the five clusters
 cluster1 <- subset(kw_with_cluster, subset = Cluster == 1)
 cluster2 <- subset(kw_with_cluster, subset = Cluster == 2)
 cluster3 <- subset(kw_with_cluster, subset = Cluster == 3)
 cluster4 <- subset(kw_with_cluster, subset = Cluster == 4)
 cluster5 <- subset(kw_with_cluster, subset = Cluster == 5)

Examining cluster 1

Print out a sample of the data:

> head(cluster1[10:13])
 Desc2 lastword firstword Cluster
 50 VintageBillboardLove/hateMug MUG VINTAGE 1
 86 BagVintagePaisley PAISLEY BAG 1
113 ShopperVintagePaisley PAISLEY SHOPPER 1
145 ShopperVintagePaisley...