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CA Calculations Demonstrated with a Simple Example

To illustrate the typical steps of a hierarchical clustering procedure and to enable the reader to follow the calculations with a pocket calculator let us assume a very simple example with five objects (named K1 to KS) and two features (x and x2) each of equal dimension. [Pg.159]

The question is Which objects may be regarded as similar with respect to both features  [Pg.159]

Because of the assumed equal dimensions the features will not be standardized. [Pg.159]

The distance matrix contains the EUCLIDean distances dik of all object pairs i and k. It is a symmetrical (5, 5)-matrix with dkl = dik, from which we write the upper triangle only  [Pg.159]

The clustering algorithm starts by joining objects K1 and K3 because their distance is the least du = 1.41. [Pg.160]


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