Data Mining Techniques: Advances and Real-World Applications
Keywords:
Knowledge Discovery in Databases (KDD), Healthcare, Cybersecurity, Knowledge Management, Machine Learning AlgorithmsAbstract
Data mining has become an essential data analysis procedure of deriving valuable information in the large and complicated data in various fields. In this review paper, the authors discuss the basic notions of data mining, the Knowledge Discovery in Databases (KDD) process, the key techniques, recent developments, challenges, and applications. The key algorithms like classification, clustering, regression, association rule mining, anomaly detection, neural networks, ensemble methods, and text mining are examined. Recent developments, such as machine learning and deep learning integration, big data platforms, real-time analytics, and AI-based search technologies, are mentioned in the study. Healthcare, finance, retail, education, cybersecurity, biological research, building energy management, and predictive analytics are only a few applications of data mining that have a wide impact on decision-making and optimization of operations. Overall, data quality, scalability, privacy, and user competency issues are critical despite the substantial improvement. The review gives a broad insight into the changing data mining methods and their current applicability in contemporary data-driven setting.
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