Clustering algorithm based on object similarity

Authors

DOI:

https://doi.org/10.26577/JMMCS2024-v123-i3-4
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Keywords:

Clustering, proximity function, degree of similarity of objects, contribution of object to the class

Abstract

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.

Author Biographies

A.Kh. Nishanov, Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, Uzbekistan, Tashkent

Nishanov Akhram Khasanovich (corresponding author) – DSc, professor of the Faculty of Software engineering of Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi (Tashkent, Uzbekistan, email: nishanov_akram@mail.ru)

M.Kh. Akbarova, Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, Uzbekistan, Tashkent

Akbarova Marguba Khamidovna – PhD, associate professor of the Faculty of Software engineering of Tashkent University of Information Technologies named after Muhammad Al- Khwarizmi (Tashkent, Uzbekistan, email: margubaakbarova66@gmail.com )

A.T. Tursunov, Tashkent Pharmaceutical Institute, Uzbekistan, Tashkent

Tursunov Alisher Tulkunovich – Senior lecturer of the Department of Physics, Mathematics and Information Technologies of Tashkent Pharmaceutical Institute (Tashkent, Uzbekistan, email: tursunovr484za@gmail.com)

F.F. Ollamberganov, Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, Uzbekistan, Tashkent

Ollamberganov Fayzulla Farxod o’g’li – PhD student Department of System and applied programming of Tashkent University of Information Technologies named after Muhammad Al- Khwarizmi (Tashkent, Uzbekistan, email: fayzulla0804@gmail.com)

D.E. Rashidova, Samarkand Institute of Economics and Service, Uzbekistan, Samarkand

Rashidova Dilfuza Elmurodovna – Teacher of the Department of Information Technologies of the Samarkand Institute of Economics and Service (Samarkand, Uzbekistan, email: dilfuzarashidova23@gmail.com)

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How to Cite

Nishanov, A., Akbarova, M., Tursunov, A., Ollamberganov, F., & Rashidova, D. (2024). Clustering algorithm based on object similarity. Journal of Mathematics, Mechanics and Computer Science, 123(3), 108–120. https://doi.org/10.26577/JMMCS2024-v123-i3-4