DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

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)
}

We can now run this on our data with:

> require(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

Thus, cluster 5 with 46 members has a high proportion of positive cases and may be a cluster we are interested in exploring further. Clusters 4, 8, and 1 are also probably worth exploring.

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)
}

And to use it:

> h <- hotspots(spam7[,1:6], clust$cluster, spam7["yesno"]=="y")

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