  In this work, the possibility of clustering correlated random variables was
examined, both because of their mutual similarity and because of their
similarity to the principal components. The k-means algorithm and spectral
algorithms were used for clustering. For spectral methods, the similarity
matrix was both the matrix of relation established on the level of correlation
and the matrix of coefficients of determination. For four different sets of
data, different ways of measuring the disimilarity of variables were analyzed,
and the impact of the diversity of initial points on the efficiency of the
k-means algorithm was analyzed.
