BoundarySeer includes two different methods for conducting spatially agglomerative clustering.
With the centroid method, the similarity between clusters is assessed through comparing average values for the clusters. That is, variables for all locations already in the cluster are averaged. A dissimilarity value is calculated for each of these "centroid" calculations, and the two clusters with the lowest dissimilarity values (i.e., the most similar) are merged in that iteration of the agglomerative clustering.
In linkage clustering, each location within a cluster is compared to each member of every other adjacent cluster. The choice of which clusters to merge can be made in many different ways. For example, you may choose single linkage clustering--agglomeration based on the minimum distance (minimum dissimilarity) calculated between any two units within two clusters. You may choose complete linkage clustering--basing the assessment of dissimilarity on the largest dissimilarity between 2 units in two clusters. Single linkage and complete linkage are the classic clustering options. Since the advent of faster computers, flexible linkage methods are now possible. Flexible linkage allows any choice in between the extremes, with a default of using the median dissimilarity (connectedness = 0.5) for comparison.
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