Analysis of the application of abstract symbols in advertising design based on cluster analysis method

[1] Lyubov., Goncharova. (2016). Cross-Cultural Aspects of Advertising Communication (on the Material of Advertising Printed Texts). Modern Communication Studies, 5(1), 18-25. Search in Google Scholar

[2] Vincenzo, G. (2016). INSIDE THE MODERN RITUALS: A SOCIAL HISTORY OF ADVERTISING. 6, 35. Search in Google Scholar

[3] Goncharova, L. (2019). “Values Are Not For Sale. Values Sell”: On the Use of Axiological Realities in Tourism Advertising. Scientific Research and Development Modern Communication Studies, 8(6), 79-83. Search in Google Scholar

[4] Lanero, A., JL, Vázquez., Sahelices-Pinto, C. (2020). Heuristic Thinking and Credibility of Organic Advertising Claims: The Role of Knowledge and Motivations. Sustainability, 12(21), 8776. Search in Google Scholar

[5] Dan, K., Auschaitrakul, S. (2020). Symbolic Sequence Effects on Consumers’ Judgments of Truth for Brand Claims. Journal of Consumer Psychology. Search in Google Scholar

[6] Ushchapovska, I., Movchan, D., Chulanova, H. (2021). Idioethnic Features of Multimodal Advertising Texts: a Case Study of Coffee Commercials. Theoretical Linguistics, 17(5), 222-236. Search in Google Scholar

[7] Itkien, R., G, Kriauiūnait-Lazauskien. (2019). The Interplay of Religious Symbols and Cultural Values Theory in Advertising. Management of Organizations: Systematic Research, 119-127. Search in Google Scholar

[8] Yang, C. M. (2019). Influences of Product Involvement and Symbolic Consumption Cues in Advertisements on Consumer Attitudes. International Journal of Marketing Studies, 11(2), 15. Search in Google Scholar

[9] Yoon, S. J., Kim, Y. E., Ock, M., et al. (2022). The gaps in health-adjusted life years (HALE) by income and region in Korea: a national representative bigdata analysis. European Journal of Public Health, Supplement_3. Search in Google Scholar

[10] Zhou, K., Fu, C., Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable & Sustainable Energy Reviews, 56, 215-225. Search in Google Scholar

[11] Fang, X., Zeng, Q., & Yang, G. (2020). Local differential privacy for data streams. Neurocomputing. Search in Google Scholar

[12] Wang, R., Zhu, Y., Chang, C. C., et al. (2020). Privacy-preserving High-dimensional Data Publishing for Classification. Computers & Security, 93, 101785. Search in Google Scholar

[13] Truex, S., Liu, L., Chow, K. H., et al. (2020). LDP-Fed: Federated Learning with Local Differential Privacy. 61-66. Search in Google Scholar

[14] Kudla., Patryk., Pawlak., et al. (2018). One-class synthesis of constraints for Mixed-Integer Linear Programming with C4.5 decision trees. Applied Soft Computing, 68, 1-12. Search in Google Scholar

[15] Saeh., IS., Mustafa., et al. (2016). Static Security classification and Evaluation classifier design in electric power grid with presence of PV power plants using C-4.5. RENEW SUST ENERG REV, 56, 283-290. Search in Google Scholar

[16] Jun, S. (2021). Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing. Sensors, 21(8), 2849. Search in Google Scholar