CLUSTERING ALGORITHM BASED ON SIMILARITY OF OBJECTS

Authors

Akhram Khasanovich Nishanov, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi. Address: Amir Temur Ave. 108, 100084, Tashkent, Republic of Uzbekistan. E-mail: nishanov_akram@mail.ru, Phone: +998935992922;Follow
Alisher Tulkunovich Tursunov, Tashkent Pharmaceutical Institute. Address: Oybek Ave. 45, 100045, Tashkent city, Republic of Uzbekistan. E-mail: tursunovr484za@gmail.com, Phone: +998884115353;Follow
Fayzullo Farhod o'g'li Ollamberganov, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi. Address: Amir Temur Ave. 108, 100084, Tashkent, Republic of Uzbekistan.Follow

Abstract

The article discusses the problem of drug clustering. Initially, k classes are randomly formed and the resulting training sample is preprocessed, then the similarity between objects of each class is assessed based on the proximity function and the criterion for assessing the contribution of objects to the formation of their own class. It is usually expressed as a percentage and represents 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 remains in the class that contributed the most. This process is repeated several times in a row for all objects of the class. The process stops when the location of objects does not change and the degree of similarity exceeds the required percentage. As a result, the necessary clusters are formed.

First Page

11

Last Page

21

References

1. Wittеn, I.H., Frank, Ye., Hall, M.A. (2011). Data mining: Practical machine learning tools and techniques. Morgan Kaufmann.
2. Arthur, D., Vassilvitskii, S. (2007). K-means++: The advantages of careful seeding. Proceedings of the yeighteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics. 1027-1035.
3. Hastie, T., Tibshirani, R., Friedman, J. (2009). The Yelements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media.
4. Yester, M., Kriegel, H.P., Sander, J., Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD). 226-231.
5. Nishanov, A. Ruzibaev, O. Tran, N. Modification of decision rules 'ball Apolonia' the problem of classification. 2016 International Conference on Information Science and Communications Technologies, ICISCT 2016. doi: 10.1109/ICISCT.2016.7777382.
6. Nishanov, A., Saidrasulov, Sh., Babadjanov, Ye. (2022). Analysis of Methodology Of Rating Yevaluation Of Digital Yeconomy And Ye-Government Development In Uzbekistan. International Journal Of Yearly Childhood Special Yeducation, 14(2), 2447-2452. doi: 10.9756/INT-JECSE/V14I2.230.
7. Nishanov, A., Ruzibaev, O., Chedjou, J.C., Kyamakya, K., Abhiram, Kolli, De Silva, Djurayev, G., Khasanova, M. (2020). Algorithm for the selection of informative symptoms in the classification of medical data. Developments Of Artificial Intelligence Technologies In Computation And Robotics. 12, 647-658. doi:10.1142/9789811223334_0078
8. Nishanov, A.Kh., Turakulov, A.Kh., Turakhanov, Kh.V. (1999). Reshaiushchee pravilo dlia klassifikatsii patologii zritel'noĭ sistemy [A decisive rule in classifying diseases of the visual system]. Med Tekh. (4):16-8. (in Russian).
9. Jain, A.K. (2010). Data clustering: 50 years beyond K-means. Pattern recognition letters. 31(8), 651-666. DOI: 10.1016/j.patrec.2009.09.011.
10. Johnson, S.C. (1967). Hierarchical clustering schemes. Psychometrika. 32(3), 241-254. DOI: 10.1007/BF02289588
11. Ester, M., Kriegel, H.P., Sander, J., Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. KDD, 96(34), 226-231.
12. Dempster, A.P., Laird, N.M., Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society. Series B (methodological), 39(1), 1-38. DOI: 10.1111/j.2517-6161.1977.tb01600.
13. Ng, A.Y., Jordan, M.I., Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. Advances in neural information processing systems, 2, 849-856.
14. Huang, X., Yang, Q., Yu, Y. (2008). Towards Context-Aware Search by Learning a Very Large Variable Length Hidden Markov Model from Search Logs. Proceedings of the 17th ACM Conference on Information and Knowledge Management - CIKM '08, 1197-1206. DOI: 10.1145/1458082.1458243.
15. Pang, B., Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1-2), 1-135. DOI: 10.1561/1500000011.
16. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167. DOI: 10.2200/S00416ED1V01Y201204HLT016.
17. Manning, C.D., Raghavan, P., Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press.

Recommended Citation

Nishanov, Akhram Khasanovich; Tursunov, Alisher Tulkunovich; and Ollamberganov, Fayzullo Farhod o'g'li (2024) "CLUSTERING ALGORITHM BASED ON SIMILARITY OF OBJECTS," Chemical Technology, Control and Management: Vol. 2024: Iss. 4, Article 2.
DOI: https://doi.org/10.59048/2181-1105.1586