In hierarchical cluster analysis, dendrograms are used to visualize how clusters are formed. I propose an alternative graph called a “clustergram” to examine how cluster members are assigned to clusters as the number of clusters increases. This graph is useful in exploratory analysis for nonhierarchical clustering algorithms such as k-means and for hierarchical cluster algorithms when the number of observations is large enough to make dendrograms impractical. I present the Stata code and give two examples.
EverittB. S., and DunnG.1991. Applied Multivariate Data Analysis.New York: John Wiley and Sons.
2.
FisherR. A.1938. The use of multiple measurements in taxonomic problems. Annals of Eugenics8: 179–188.
3.
HandD., MannilaH., and SmythP.2001. Principles of Data Mining.Cambridge, MA: MIT Press.
4.
JohnsonR. A., and WichernD.W.1988. Applied Multivariate Analysis. 2d ed. Englewood Cliffs, NJ: Prentice–Hall.
5.
MacQueenJ.1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds. LeCamL. M., and NeymanJ., vol. 1, 281–297. Berkeley: University of Caifornia Press.
