Hierarchical agglomerative algorithms

Further details of agglomerative, and several other clustering strategies may be found in the book by MASSART and KAUFMAN [1983] or, along with remarks on the treatment of situations with missing values, in the monograph by MUCHA [1992]. Finally, it may be of interest that OZAWA [1983] even proposed a hierarchical cluster algorithm based on an asymmetric distance matrix. 

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-... 

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.

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