Advances in Big Data and Data Mining: Techniques and Applications in Data Fusion for Enhanced Insights and Decision-Making

Big data's explosive growth has transformed several businesses by making it possible to glean insightful information from big, complex databases. This study examines new developments in big data and data mining methods, concentrating on data fusion as a means of improving decision-making. Data fusion, the act of combining many data sources to get more accurate and thorough insights, is becoming essential in industries including cybersecurity, healthcare, finance, and marketing. The study looks at how cutting-edge data mining methods, such as anomaly detection, classification, and clustering, might increase the efficiency of data fusion. Important issues are also covered, including managing heterogeneous data, guaranteeing data quality, and scalability. This paper shows how data fusion approaches may enhance decision-making processes by offering more comprehensive perspectives and useful insights via an analysis of real-world applications. The paper's conclusion discusses potential future developments, emphasising how machine learning and artificial intelligence may be used to improve data mining and fusion procedures, potentially leading to even higher levels of precision and effectiveness in decision-making across a range of industries.