Abstract
The paper developed a model of cluster analysis of expert criteria for assessing qualitative (non-numerical) characteristics with a certain level of reliability. To formalize individual criteria, Z-numbers are used, which are ordered pairs of ordinary fuzzy numbers. The first number is an estimate of the characteristic, and the second number is its reliability. For Z-numbers and their first components the aggregating indicators was defined based on a-cuts of fuzzy numbers. Aggregating indicators are used to determine the pairwise difference indexes of expert criteria and pairwise similarity indexes. Based on pairwise similarity indexes of expert criteria, a fuzzy binary relation of analogy is determined on the set of all criteria. A fuzzy similarity relation on a set of criteria is built using the transitive closure of a fuzzy relation of analogy. The constructed fuzzy similarity relation is decomposed into equivalence relations. Depending on the levels of values of the fuzzy similarity relation, all individual expert criteria are divided into clusters of similar criteria with a-levels of reliability.
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