DATA MINING
Desktop Survival Guide by Graham Williams |
|||||
Hot Spots |
Cluster analysis can be used to find clusters that are most
interesting according to some criteria. For example, we might
cluster the spam7 data of the DAAG package (without using yesno in the
clustering) and then score the clusters depending on the proportion of
yes cases within the cluster. The following R code will build K
clusters (user specified) and return a score for each cluster.
# Some ideas here from Felix Andrews kmeans.scores <- function(x, centers, cases) { clust <- kmeans(x, centers) # Iterate over each cluster to generate the scores scores <- c() for (i in 1:centers) { # Count number of TRUE cases in the cluster # as the proportion of the cluster size scores[i] <- sum( cases[clust$cluster == i] == TRUE ) / clust$size[i] } # Add the scores as another element to the kmeans list clust$scores <- scores return(clust) } |
> library(DAAG) > data(spam7) > clust <- kmeans.scores(spam7[,1:6], centers=10, spam7["yesno"]=="y") > clust[c("scores","size")] $scores [1] 0.7037037 0.1970109 0.5995763 0.7656250 0.8043478 1.0000000 0.4911628 [8] 0.7446809 0.6086957 0.6043956 $size [1] 162 2208 472 128 46 5 1075 47 276 182 |
Now that we have built some clusters we can generate some rules that
describe the clusters:
hotspots <- function(x, cluster, cases) { require(rpart) overall = sum(cases) / nrow(cases) x.clusters <- cbind(x, cluster) tree = rpart(cluster ~ ., data = x.clusters, method = "class") # tree = prune(tree, cp = 0.06) nodes <- rownames(tree$frame) paths = path.rpart(tree, nodes = nodes) TO BE CONTINUED return(tree) } |
> h <- hotspots(spam7[,1:6], clust$cluster, spam7["yesno"]=="y") |