This white paper presents a comparative approach to anomaly detection using DBSCAN clustering and Isolation Forest within Altair AI Studio. It explains how unsupervised machine learning identifies irregular patterns in transactional and operational data without labeled examples. The paper details data preparation, feature engineering, model tuning, and visualization workflows in a low-code environment. Realistic use cases demonstrate how organizations can detect fraud, operational anomalies, and unusual behavior more effectively while improving interpretability and scalability.

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