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

Basic Clustering

We illustrate very simple clustering through a complete example where the task is to read data from a file (See Section ), extract the numeric fields, and then use k-means (See Chapter ) to cluster on just two columns. A plot of the clusters over the two columns shows the points and the cluster centroids. Normally, the clusters would be built over more than just two columns. Also note that each time the code is run a different clustering is likely to be generated!

clusters <- 5
load("wine.Rdata")
pdf("graphics/rplot-cluster.pdf")
  wine.cl = kmeans(wine[,2:3], clusters)
  plot(wine[,2:3], col=wine.cl$cluster)
  points(wine.cl$centers, pch=19, cex=1.5, col=1:clusters)
dev.off()

http://rattle.togaware.com/code/rplot-cluster.R

The resulting cluster entity has the following entries:

cluster:	The cluster that each row belongs to.
centers:	The medoid of each cluster.
withinss:	The within cluster sum of squares.
size:	The size of each cluster.

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