Agglomerative clustering techniques

Singh, W. (2008). Robustness of three hierarchical agglomerative clustering techniques for ecological data. M.Sc. thesis. University of Iceland, Iceland. 

There are two main types of clustering techniques hierarchical and nonhierarchical. Hierarchical cluster analysis may follow either an agglomerative or a divisive scheme agglomerative techniques start with as many clusters as objects and, by means of repeated similarity-based fusion steps, they reach a final situation with a unique cluster containing all of the objects. Divisive methods follow exactly the opposite procedure they start from an all-inclusive cluster and then perform a number of consecutive partitions until there is a bijective correspondence between clusters and objects (see Fig. 2.12). In both cases, the number of clusters is defined by the similarity level selected. 

The outcome of agglomerative hierarchical cluster analysis is a crisp cluster membership function, which can take only the values 0 (no membership) or 1 (membership). Other non-hierarchical clustering techniques such as k-means cluster (KMC) analysis still follow this concept, whereas fuzzy C-means (FCM) clustering returns fuzzy class memberships. The latter method thus departs from the classical (0 or 1) two-valued logic and uses soft linguistic system variables, i.e. degrees of class membership values varying between 0 and 1. 

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