Ranking of objects based on fuzzy clustering

The paper creates a method for using fuzzy logic in the task of dividing objects into clusters and ranking them, where their coordinates are indicated as features. Classical machine learning approaches for making clustering models are investigated. Further, they are supplemented with the fuzzy numbers apparatus to obtain an estimate of the potential possibility of an object belonging to a cluster. Based on the selected approaches, algorithmic and software were developed for assigning an object to clusters with the derivation of the membership function, as well as the derivation of the rank calculated through defuzzification, taking into account the importance of each cluster. The resulting model can be used to solve the problems of selecting and ranking objects, taking into account the degree of confidence in their belonging to certain classes.