Agnes proceeds by a series of fusions. Initially (at step 0), all objects are apart– each object forms a small cluster by itself. At the first step, two closest or minimally dissimilar objects are merged to form a cluster. Then the algorithm finds a pair of objects with minimal dissimilarity. If there are several pairs with minimal dissimilarity, the algorithm picks a pair of objects at random. Picking of pairs of objects, which are minimally dissimilar is quite straightforward, but how to select pairs of clusters with minimal dissimilarity? It is necessary to define a measure of dissimilarity between clusters. Average pair group method (UPGMA) is used to compute the dissimilarity between a pair of clusters. Dissimilarity between clusters R and Q is defined as the average of all dissimilarities:
d (i ,j) , where i is any object of R and j is any object of Q. More formally
d ( R, Q) = [1/ (|R||Q|)]
where |R| and |Q| denote the number of objects in clusters R and Q respectively.
Agnes computes a coefficient, called Agglomerative Coefficient, which measures the clustering structure of the data set. Agglomerative coefficient is defined as follows:
Let d(i) denote the dissimilarity of object i to the first cluster it is merged with, divided by the dissimilarity of the merger in the last step of the algorithm.
The agglomerative coefficient (AC) is defined as the average of all [ 1-d(i)]
Note that the agglomerative coefficient (AC) defined above can also be defined as the average width (or the percentage filled) of the banner plot described below.
Because AC grows with the number of objects, this measure should not be used to compare clusters of data sets of very different sizes.
Dissimilarity Banner: The hierarchy obtained by Agnes can be graphically displayed by means of a banner, which shows the successive mergers from left to right A banner consists of stars and stripes. The stars indicate linking of objects and stripes are identifiers of these objects. A banner is always read from left to right. Each line with stars starts at the dissimilarity between the clusters being merged. There are fixed scales above and below the banner, ranging from 0.00 (Dissimilarity = 0) and 1.00 (Dissimilarity = highest dissimilarity encountered). The actual highest dissimilarity corresponding to 1.00 in the banner is shown just below the banner.
The overall width of the banner gives an idea of the amount of structure found by the algorithm. When the data possesses a clear cluster structure, the between - cluster dissimilarities would become larger than the within cluster dissimilarities. As a consequence, the black lines in the banner would become larger.
The average width of the banner is called the agglomerative coefficient (AC). It describes the strength of the clustering structure that has been found.
where li is the length of the line containing the identifier of object i. AC is a dimensionless quantity, varying between 0 and 1. AC close to 1 indicates that a very clear structuring has been found. AC close to 0 indicates that the algorithm has not found a natural structure. In other words, the data consists of only one big cluster.