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

Hierarchical Clustering

Agglomerative clustering is used to build a hierarchical cluster. A complete hierarchical cluster is built on the click of the Execute button. You do not need to re-execute on changing the Number of Clusters. This simply needs to obtain the relevant information from the fully built hclust. But users will automatically go to re-execute after changing this (because this is how everything else in the interface works). An alternative is being considered to make it more obvious not to re-execute.

Once a cluster has been built, have a look at the dendrogram to visually get an idea of the ``natural'' number of clusters, and then set the number appropriately, then have a look at the stats and the plot.

The amap package includes standard hierarchical clustering with a choice of distances like Eulidean and Spearman, and a parallel implementation.

Support further development through the purchase of the PDF version of the book.
Brought to you by Togaware.