Joining (Tree Clustering) Introductory Overview - Hierarchical Tree

Consider a Horizontal Hierarchical Tree Plot, available from the Quick tab of the Joining Results dialog. On the left of the plot, we begin with each object in a class by itself. Now imagine that, in very small steps, we "relax" our criterion as to what is and is not unique. Put another way, we lower our threshold regarding the decision when to declare two or more objects to be members of the same cluster. As a result we link more and more objects together and aggregate (amalgamate) larger and larger clusters of increasingly dissimilar elements. Finally, in the last step, all objects are joined together.

In these plots, the horizontal axis denotes the linkage distance (in Vertical Icicle Plots, also available from the Quick tab of the Joining Results dialog the vertical axis denotes the linkage distance). Thus, for each node in the graph (where a new cluster is formed) we can read off the criterion distance at which the respective elements were linked together into a new single cluster. When the data contain a clear "structure" in terms of clusters of objects that are similar to each other, then this structure will often be reflected in the hierarchical tree as distinct branches. As the result of a successful analysis with the joining method, one is able to detect clusters (branches) and interpret those branches.

For an overview of the other two methods of clustering, see Two-way Joining and K-means Clustering.