IMPLEMENTATION OF UNSUPERVISED LEARNING MODELS FOR ANALYZING THE STATE'S SECURITY LEVEL
Main Article Content
Abstract
Objective. Enhancing the effectiveness of preliminary analysis of the state's security level through the implementation of clustering models. Methodology. The process of creating unsupervised learning models and their peculiarities in tasks of analyzing the state's security level has been investigated. Techniques for creating the basic k-means model and its improvement through the use of Pearson correlation as a distance metric have been considered. Determining cluster centers was performed by both the basic method and the Cochran's maps method. The optimal quality indicator, according to the results of clustering, was considered to be the model demonstrating the minimum value of the Davies-Bouldin index. Results. An improved unsupervised learning model based on the k-means algorithm for analyzing the state's security level has been developed. The model is characterized by two clusters, with centroids determined as 1.112 and 1.009. Scientific novelty. The proposed model for clustering the state's security level differs from existing ones by using as input estimates derived from a comprehensive indicator based on the principles of interaction and emergent properties. This allows obtaining advantages of the clustering model in terms of the Davies-Bouldin index. The existing clustering model demonstrates a value of 0.4765, while the proposed one achieves 0.2166. Practical significance. The proposals serve as a useful additional tool for preliminary analysis of the state's security level during air alerts and extend the functionality of the previously researched forecasting technology.
Article Details
References
Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B. and Heming, J. (2022), “K-means Clustering Algorithms: A Comprehensive Review, Variants Analysis, and Advances in the Era of Big Data”, Information Sciences, doi: https://doi.org/10.1016/j.ins.2022.11.139
Krasnobayev V., Yanko A. and Hlushko A. (2023), “Information Security of the National Economy Based on an Effective Data Control Method”, Journal of International Commerce, Economics and Policy, vol. 14, no. 03, 2350021, doi: https://doi.org/10.1142/s1793993323500217
Kuchuk, G., Kovalenko, A., Komari, I.E., Svyrydov, A. and Kharchenko, V. (2019), “Improving Big Data Centers Energy Efficiency: Traffic Based Model and Method”, In: Kharchenko V., Kondratenko Y., Kacprzyk J. (eds) Green IT Engineering: Social, Business and Industrial Applications. Studies in Systems, Decision and Control, vol 171, Springer, Cham, doi: https://doi.org/10.1007/978-3-030-00253-4_8
Petrovska, I. and Kuchuk, H. (2023), “Adaptive resource allocation method for data processing and security in cloud environment”, Advanced Information Systems, vol. 7 (3), pp. 67–73, doi: https://doi.org/10.20998/2522-9052.2023.3.10
Kuchuk, G., Nechausov, S. and Kharchenko, V. (2015), “Two-stage optimization of resource allocation for hybrid cloud data store”, International Conference on Information and Digital Technologies, Zilina, pp. 266–271, doi: http://dx.doi.org/10.1109/DT.2015.7222982
Onyshchenko S., Yanko A. and Hlushko A. (2023), “Improving the efficiency of diagnosing errors in computer devices for processing economic data functioning in the class of residuals”, Eastern-European Journal of Enterprise Technologies, vol. 5, no. 4 125), pp. 63–73, doi: https://doi.org/10.15587/1729-4061.2023.289185
Dotsenko, N., Chumachenko, I., Galkin, A., Kuchuk, H. and Chumachenko, D. (2023), “Modeling the Transformation of Configuration Management Processes in a Multi-Project Environment”, Sustainability (Switzerland), vol. 15 (19), 14308, doi: https://doi.org/10.3390/su151914308
Shtompel, M., Prykhodko, S., Shefer, O., Halai, V., Zakharchenko, R., and Topikha, B. (2020), “Performance analysis of the bioinspired method for optimizing irregular codes with a low density of parity checks”, Eastern-European Journal of Enterprise Technologies, vol. 6, no. 9 (108), pp. 34–41, doi: https://doi.org/10.15587/1729-4061.2020.216762
Kuchuk, H., Mozhaiev, O., Kuchuk, N., Tiulieniev, S., Mozhaiev, M., Gnusov, Y., Tsuranov, M., Bykova, T., Klivets, S., and Kuleshov, A. (2024), “Devising a method for the virtual clustering of the Internet of Things edge environment”, Eastern-European Journal of Enterprise Technologies, vol. 1, no. 9 (127), pp. 60–71, doi: https://doi.org/10.15587/1729-4061.2024.298431
Zhang, H., Li, J., Zhang, J. and Dong, Y. (2024), “Speeding up k-means clustering in high dimensions by pruning unnecessary distance computations”, Knowledge-Based Systems, vol. 284, 111262, doi: https://doi.org/10.1016/j.knosys.2023.111262
Kuchuk, N., Mozhaiev, O., Semenov, S., Haichenko, A., Kuchuk, H., Tiulieniev, S., Mozhaiev, M., Davydov, V., Brusakova, O. and Gnusov, Y. (2023), “Devising a method for balancing the load on a territorially distributed foggy environment”, Eastern-European Journal of Enterprise Technologies, vol. 1, no. 4 (121), pp. 48–55, doi: https://doi.org/10.15587/1729-4061.2023.274177
Zhou, Q. and Sun, B. (2023), “Adaptive K-means clustering based under-sampling methods to solve the class imbalance problem”, Data and Information Management, 100064, doi: https://doi.org/10.1016/j.dim.2023.100064
Palakonda, V., Kang, J.-M. and Jung, H. (2023), “An Effective Ensemble Framework for Many-Objective Optimization based on AdaBoost and K-means Clustering”, Expert Systems with Applications, 120278, doi: https://doi.org/10.1016/j.eswa.2023.120278
Kumar, Abhimanyu, Kumar, Abhishek, Mallipeddi, R. and Lee, D.-G. (2024), “High-density cluster core-based k-means clustering with an unknown number of clusters:, AApplied Soft Computing, vol. 155, 111419, doi: https://doi.org/10.1016/j.asoc.2024.111419
Li, G., Teng, Yi., Ding, S. and Hou X. (2024), “Complex mechanical system safety prediction based on multidimensional indexes: An MBSA-PCA-BPNN method”, Engineering Failure Analysis, vol. 159, 108130, doi: https://doi.org/10.1016/j.engfailanal.2024.108130
Coraça, E. M., Ferreira, J. V. and Nóbrega, E. G. O. (2023), “An unsupervised structural health monitoring framework based on Variational Autoencoders and Hidden Markov Models”, Reliability Engineering & System Safety, vol. 231, 109025, doi: https://doi.org/10.1016/j.ress.2022.109025
Fathi, M. and Bolandi, H. (2024), “Unsupervised optimal model bank for multiple model control systems: Genetic-based automatic clustering approach”, Heliyon, e25986. doi: https://doi.org/10.1016/j.heliyon.2024.e25986
Rodriguez, M. Z., Comin, C. H., Casanova, D., Bruno, O. M., Amancio, D. R., Costa, L. da F. and Rodrigues, F. A. (2019), “Clustering algorithms: A comparative approach”, PLOS ONE vol. 14, no. 1, e0210236, doi:
https://doi.org/10.1371/journal.pone.0210236
Pietrzykowski, M. (2019), “Mini-models based on soft clustering methods”, Procedia Computer Science, vol. 159, pp. 2512–2521, doi: https://doi.org/10.1016/j.procs.2019.09.426
Yang, X., Zhao, W., Xu, Yu., Wang, C.-D., Li, B. and Nie F. (2019), “Sparse K-means clustering algorithm with anchor graph regularization”, Information Sciences, vol. 667, 120504. doi: https://doi.org/10.1016/j.ins.2024.120504
Pu, Yu., Yao, W., Li, X. and Alhudhaif A. (2024), “An adaptive highly improving the accuracy of clustering algorithm based on kernel density estimation”, Information Sciences, vol. 663, 120187, doi: https://doi.org/10.1016/j.ins.2024.120187
Yu, H., Xu, X., Li, H., Wu, Yu. and Lei B. (2024), “Semi-Supervised Possibilistic c-Means Clustering Algorithm Based on Feature Weights for Imbalanced Data”, Knowledge-Based Systems, vol. 286, 28 February 2024, 111388, doi: https://doi.org/10.1016/j.knosys.2024.111388
Akhter, M. M. and Mohanty, S. K. (2023), “A fast O(NlgN) time hybrid clustering algorithm using the circumference proximity based merging technique for diversified datasets”, Engineering Applications of Artificial Intelligence, vol. 125, 106737, doi: https://doi.org/10.1016/j.engappai.2023.106737
Xu, S., Hao, Z., Zhu, Yu., Wang, Z., Xiao, Yu. and Liu B. (2023), “Semi-Supervised Fuzzy Clustering Algorithm Based on Prior Membership Degree Matrix with Expert Preference”, Expert Systems with Applications, 121812, doi: https://doi.org/10.1016/j.eswa.2023.121812
Gorokhovatskyi, O. and Yakovleva, O. (2024), “Medoids as a packing of orb image descriptors”, Advanced Information Systems, vol. 8, no. 2, pp. 5–11, doi: https://doi.org/10.20998/2522-9052.2024.2.01
Branco, D. P. P. and de A.T. De Carvalho F. (2023), “Medoid based semi-supervised fuzzy clustering algorithms for multi-view relational data”, Fuzzy Sets and Systems, 108630, doi: https://doi.org/10.1016/j.fss.2023.108630
Vishwakarma, G.K., Paul, C., Hadi, A.S. and Elsawah A.M. (2023), “An automated robust algorithm for clustering multivariate data”, Journal of Computational and Applied Mathematics, vol. 429, 115219, September 2023, doi: https://doi.org/10.1016/j.cam.2023.115219
Shefer, O., Laktionov, O., Pents, V., Hlushko, A. and Kuchuk, N. (2024), “Practical principles of integrating artificial intelligence into the technology of regional security predicting, Advanced Information Systems, vol. 8, no. 1, pp. 86–93, doi: https://doi.org/10.20998/2522-9052.2024.1.11
Khoroshun, G., Ryazantsev, O. and Cherpitskyi, M. “(2023), “Clustering and anomalies of USA stock market volatility index data”, Advanced Information Systems, vol. 7, no. 2, pp. 9–15, doi: https://doi.org/10.20998/2522-9052.2023.2.02
Campbell, A. (2020), Python Guide: Clear Introduction to Python Programming and Machine Learning, Independently Published, 278 p., available at: https://books.google.com.ua/books/about/Python_Guide.html?id=7NHYzQEACAAJ&redir_esc=y
(2024), “Scikit-learn: machine learning in Python – scikit-learn 1.4.1 documentation”, Scikit-learn: machine learning in Python – scikit-learn 0.16.1 documentation, available at: https://scikit-learn.org/stable/
(2024), MiniSom. PyPI, available at: https://pypi.org/project/MiniSom/