Abstract
Abstract Classically, unsupervised machine learning techniques are applied on data sets with fixed number of attributes (variables). However, many problems encountered in the field of informetrics face us with the need to extend these kinds of methods in a way such that they may be computed over a set of nonincreasingly ordered vectors of unequal lengths. Thus, in this paper, some new dissimilarity measures (metrics) are introduced and studied. Owing to that we may use, e.g. hierarchical clustering algorithms in order to determine an input data set's partition consisting of sets of producers that are homogeneous not only with respect to the quality of information resources, but also their quantity.
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