The phenomenon of economic globalization has led to the swift advancement of industries across diverse domains. Consequently, big data technology has garnered increasing interest. The generation of network data is occurring at an unparalleled pace, necessitating the intelligent processing of vast amounts of data. To fully leverage the value inherent in this data, the implementation of machine learning techniques is imperative. The objective of machine learning in a vast data setting is to identify particular rules that are concealed within dynamic, variable, multi-origin heterogeneous data, with the ultimate aim of maximizing the value of the data. The integration of big data technology and machine learning algorithms is imperative in order to identify pertinent correlations within intricate and dynamic datasets. Subsequently, computer-based data mining can be utilized to extract valuable research insights. The present study undertakes an analysis of deep learning in comparison to conventional data mining and machine learning techniques. It conducts a comparative assessment of the strengths and limitations of the traditional methods. Additionally, the study introduces the requirements of enterprises, their systems and data, the IT challenges they face, and the role of Big Data in an extended service infrastructure. This study presents an analysis of the probability and issues associated with the utilization of deep learning, including machine learning and traditional data mining techniques, in the big data analytics context.
Keywords
Machine Learning, Big Data, Data Mining, Big Data Analytics, Traditional Data Mining.
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Cite this article
Francisco Pedro, “A Review of Data Mining, Big Data Analytics, and Machine Learning Approaches”, Journal of Computing and Natural Science, vol.3, no.4, pp. 169-181, October 2023. doi: 10.53759//181X/JCNS/202303016.
