(untitled) (Journal of Mathematics Mechanics and Computer Science, 2024)
The article examines the issue of drug clustering. Initially, k classes are arbitrarily formed and the resulting training sample is pre-processed, then the similarities between the objects of each class are evaluated based on the proximity function and the criterion for evaluating the contribution of objects to the formation of their own class. Usually, it is in percentage and is the degree of mutual similarity of objects of each class. In the next steps of the algorithm, first, one object is taken from the first class, and by adding it to all k classes, the contribution of this object to this class is measured. The object will be left in the class which has the most contribution. This process is repeated several times in a row for all objects of the class. The process is stopped when the location of objects does not change and the degree of similarity exceeds the required percentage. As a result, the required clusters are formed.