Welcome to Scholar Publishing Group

Machine Learning Theory and Practice, 2023, 4(1); doi: 10.38007/ML.2023.040104.

Business Application of Machine Learning Technology in Data Mining


Jin Zhao

Corresponding Author:
Jin Zhao

Lyceum of the Philippines University, Philippines


In today's business field, data mining technology has become an indispensable part, and it is also a hot research direction. This paper mainly discusses and analyzes machine learning algorithm. First, we will introduce the classification, clustering and classification models of biological samples. Secondly, we will briefly discuss two different methods based on artificial neural network and hybrid Bayesian tree algorithm in theory. Finally, we will draw a conclusion through experimental comparison that the performance of machine learning in business application models in data mining is relatively reasonable, and the model processing time is relatively short, which meets the needs of users. At the same time, this paper also puts forward relevant suggestions to enhance the application value of this technology in business, so as to provide some help for enterprises to mine effective information.


Machine Learning, Data Mining, Business Application, Learning Technology

Cite This Paper

Jin Zhao. Business Application of Machine Learning Technology in Data Mining. Machine Learning Theory and Practice (2023), Vol. 4, Issue 1: 27-34. https://doi.org/10.38007/ML.2023.040104.


[1]  Bhaskar G, MD Reddy, Thatikonda M. A Review on Secure Data Transmission for Banking Application using Machine Learning. International Journal of Engineering and Advanced Technology, 2021, 10(5):182-186.

[2] Dogra A K, Kaur J. Moving towards smart transportation with machine learning and Internet of Things (IoT): a review. Journal of Smart Environments and Green Computing, 2022, 2(1):3-18. 

[3] Lu H. Application of wireless network and machine learning algorithm in entrepreneurship education of remote intelligent classroom. Journal of Intelligent and Fuzzy Systems, 2021, 40(2):2133-2144.

[4]  Krischke U. Rhona Alcorn, Joanna Kopaczyk, Bettelou Los and Benjamin Molineaux (eds.). 2019. Historical Dialectology in the Digital Age. Edinburgh: Edinburgh University Press, xvii + 274 pp. 42 figures, 33 tables, 80.00.. Anglia, 2020, 138(1):166-170.

[5] S Sigurjonsdottir, Nowenstein I. Language acquisition in the digital age: L2 English input effects on children's L1 Icelandic:. Second Language Research, 2021, 37(4):697-723.

[6]  Sihite M, Manullang S O, Nugroho B S. Relevance of mastery of information systems skills and success of business management in the digital age: a systematic review. International Journal of Social Sciences and Humanities, 2021, 5(2):68-78. 

[7] M Papík, L Papíková. Application of selected data mining techniques in unintentional accounting error detection. Equilibrium, 2021, 16(1):185-201. 

[8] Dadouh A, Aomari A. Moroccan TV Broadcasters and Viewership Changes in the Digital Age: An Exploratory Study. European Journal of Business Management and Research, 2021, 6(1):232-237.

[9] Wang J, Cao S J, Yu C W. Development trend and challenges of sustainable urban design in the digital age:. Indoor and Built Environment, 2021, 30(1):3-6.

[10] Mandal P C. Public Policy Issues and Technoethics in Marketing Research in the Digital Age. International Journal of Technoethics, 2021, 12(1):75-86. 

[11]  Rahmat A, Syakhrani A W, Satria E. Promising online learning and teaching in digital age: systematic review analysis. International Research Journal of Engineering IT & Scientific Research, 2021, 7(4):126-135.

[12]  Razmetaeva Y S, Sydorenko O O. Abortion, Human Rights And Medical Advances In Digital Age. Wiadomości lekarskie (Warsaw, Poland: 1960), 2021, 74(1):132-136. 

[13] Vartouni A M, Teshnehlab M, Kashi S S. Leveraging Deep Neural Networks for Anomaly-Based Web Application Firewall. IET Information Security, 2019, 13(4):352-361. 

[14] Kadiri K, Oluwatoyin I N, Akanbi I I. Design of deep learning system for agricultural purpose. International Journal of Information and Communication Technology, 2021, 2(1):30-35. 

[15]  Cybulski J L, Scheepers R. Data science in organizations: Conceptualizing its breakthroughs and blind spots. Journal of Information Technology, 2021, 36(2):154-175. 

[16] Kohsaka R, Fujihira Y, Uchiyama Y. Biomimetics for business? Industry perceptions and patent application. Journal of Science and Technology Policy Management, 2019, 10(3):597-616. 

[17]  Rana M E, Wang W. A Machine Learning based Software Project Schedule Management Solution. Test Engineering and Management, 2020, 83(1):307-321. 

[18]  Mimoun J. Technology Focus: Well Testing (February 2021). Journal of Petroleum Technology, 2021, 73(2):51-51.