The metrologically reasonable clustering of measurement results

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.

• Publication year

  2019

• Venue

  Journal of Physics: Conference Series

• Publication date

  2019-11-01

• Fields of study

  Physics, Computer Science

• Identifiers
  DOI 10.1088/1742-6596/1379/1/012054
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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