Big Data Mining and Analysis: Methods, Challenges, Applications, and Future Trends

Data mining and analysis of Big Data are now indispensable approaches for capturing important information from large data sets in different industries. This paper aims to give an overview on the methods, issues, and uses of Big Data mining including classification, clustering, regression, and anomaly detection. In this research, we explore the basic principles and tools underlying Big Data, including Hadoop and Spark and cloud-based solutions. The paper also covers the issues regarding data quality, data scalability, privacy and real time processing and the solutions that have been worked out to contain these problems. Big Data mining applications in healthcare, finance, e-commerce, social media, smart cities, and IoT are described to illustrate the impact of Big Data on industries and its ability to foster change. In the future, the Big Data analysis will be expanded with the help of Machine Learning, AI, edge computing, and quantum computing. Ethical issues are also discussed with relation to data management and new regulations. In conclusion, this survey calls for more iteration between disciplines to advance the Big Data mining and analysis effectively and ethically across industries.