Anomaly detection plays a pivotal role in data-driven machine learning applications, enabling the identification of rare or unexpected patterns that deviate from the norm. These anomalies, which can indicate critical events such as fraud, security breaches, equipment failures, or medical conditions, are invaluable in a variety of fields. This paper provides an in-depth review of anomaly analytics, focusing on the various techniques used in machine learning to detect anomalies in complex, high-dimensional data. We explore statistical methods, machine learning-based approaches, and hybrid models, analyzing their strengths and weaknesses across multiple domains including cybersecurity, finance, healthcare, and manufacturing. The paper also discusses key evaluation metrics for anomaly detection and highlights the challenges of scalability, noise handling, and model interpretability. Finally, we examine emerging trends in anomaly detection, including real-time processing and explainability, and suggest future research directions to improve the robustness and efficiency of anomaly detection systems in large-scale, dynamic environments. This work serves as a comprehensive guide for understanding the role of anomaly analytics in modern machine learning applications, offering insights into current methodologies and future advancements.

Anomaly Detection, Machine Learning, Data Streams, Outlier Detection, Unsupervised Learning

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