This paper is dedicated to the metrological aspects of clustering procedure. It is shown that taking into account the information on uncertainty of the data to be clustered allows easily making reasonable decisions that are metrologically supported. The paper presents that the problems that are usually difficult to solve in practice of the clustering become simpler: the proposed approach allows determining the number of maximum recognizable clusters and thus protects from the unreasonable conclusions and allows performing the preconditioning of data to be clustered simpler.

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