  There are many distance-based methods for classification and clustering, and
for data with a high number of dimensions and a lower number of observations,
processing distances is computationally advantageous compared to the raw data
matrix. Euclidean distances are used as a default for continuous multivariate
data, but there are alternatives. Here the so-called Minkowski distances, $L_1$
(city block)-, $L_2$ (Euclidean)-, $L_3$-, $L_4$-, and maximum distances are
combined with different schemes of standardisation of the variables before
aggregating them. Boxplot transformation is proposed, a new transformation
method for a single variable that standardises the majority of observations but
brings outliers closer to the main bulk of the data. Distances are compared in
simulations for clustering by partitioning around medoids, complete and average
linkage, and classification by nearest neighbours, of data with a low number of
observations but high dimensionality. The $L_1$-distance and the boxplot
transformation show good results.
