EMPIRICAL EVALUATION IN DATA MINING IN ASSOCIATION WITH ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Abstract

Data analysis and decision-making have undergone a dramatic revolutionize as a consequence of modern improvements in data mining, which are driven by Artificial Intelligence, Machine Learning, and Deep Learning. Artificial Intelligence and Machine Learning approaches are increasingly being used to supplement and improve traditional data mining techniques as the dimensions and involvedness of information persist to increase. With an emphasis on advancements generated by AI and ML, this article examines the most recent research trends in data mining. The usage of Auto-ML to automate machine learning processes, Federated Learning for privacy-preserving data mining, and Explainable AI (XAI) to increase model transparency are important areas of study. More dynamic and scalable solutions are also being made possible by the combination of multimodal data mining, graph-based algorithms, and real-time analytics at the edge. In response to ethical concerns about AI-driven decision-making systems, efforts to reduce bias, maintain justice, and protect privacy have also gained traction. More precise, scalable, and effective solutions are becoming possible in a variety of segments, such as healthiness care, economics, and security, thanks to the convergence of Artificial Intelligence, Machine Learning, along with data mining. Future studies will probably concentrate on resolving issues with explainability, privacy, and processing more complicated, unstructured data as these technologies develop further. This research paper provides an indication of these budding trends and discusses the impending directions for expectations research in the sector of information mining in perspective of AI and ML.