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On the clustering of correlated random variables

Gniazdowski Z., Kaliszewski D.

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki

2018

nr 18

45--114

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.