DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Data Mining Introduction Getting Started Data Loading Data Exploring Data Interactive Graphics Test Descriptive Data Mining Predictive Data Mining Evaluation and Deployment Data Cleaning Handling Missing Data Transforming Data Data Reduction Deployment Troubleshooting Issues Moving into R Beyond Rattle R Getting Help Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Topics in Data Mining Fraud Analysis Archetype Analysis Text Mining Survival Analysis Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Support Vector Machines 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 Appendices Installing Rattle Bibliography Index Preface Goals Organisation Features Audience Typographical Conventions A Note on Languages Currency Acknowledgements

Basic Clustering

We illustrate very simple clustering through a complete example where the task is to read data from a file (See Section 21.3.4), extract the numeric fields, and then use k-means (See Chapter 41.2) 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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