Hierarchic agglomerative clustering

The principle of unsupervised learning consists in the partition of a data set into small groups to reflect, in advance, unknown groupings [YARMUZA, 1980] (see also Section 5.3). The results of the application of methods of hierarchical agglomerative cluster analysis (see also [HENRION et al., 1987]) were representative of the large palette of mathematical algorithms in cluster analysis. 

Results from hierarchical agglomerative cluster analysis according to the algorithm of WARD (see Section 5.3) are illustrated as a dendrogram in Fig. 7-14. Distinction of the months in which the heating of buildings has a large influence from the summer months is clearly demonstrated. November and December of the second year of the in-... 

Fig. 7-14. Dendrogram of the hierarchical agglomerative cluster analysis according to WARD...

The data matrix is subjected to hierarchical agglomerative cluster analysis (CA for the mathematical fundamentals see Section 5.3 further presentation of the algorithms is given by [HENRION et al., 1987]) in order to find out whether territorial structures with different multivariate patterns of heavy metals exist within the test area. 

Figure 1. Stored matrix algorithm for hierarchic agglomerative clustering methods.

Hierarchical Agglomerative Clustering Package. Barnard Chemical Information Ltd. http //www.bci.gb.com. 

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

In order to better understand the different steps of cluster analyses, hierarchical (agglomerative) clustering is demonstrated here for a data set from clinical analysis. 

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