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\subsection{Hierarchical Clustering using Ward's method}  Hierarchical clustering is yet another unsupervised algorithm for determining the clustered groups, yet it uses a much simpler method, namely merging the two closest clusters, and updating the proximity matrix to reflect the proximity between the new cluster and the original clusters, until there is only one large cluster left. For the cars dataset, Ward's method, or minimizing method (i.e. the method which minimizes  the sum variance  of squared the  distance of the observations  to the new center after merging two clusters, clusters centers)  was used with an Euclidean metric as the similarity distance  measure between the observations. observations and the clusters centers.  A dendrogram showing the results of this clustering method can be seen in the following figure. Figure \ref{DendrogramPCAData}.