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Data Science in Service of Community Anomaly Detection: Shaping Strategy Based on Discovered Patterns of Deviant Phenomena
Abstract
This study explores the strategic implications of identifying and analyzing patterns in deviant phenomena across various domains. By leveraging advanced data science techniques, including Benford's Law analysis, Bayesian networks, and extreme value theory, we uncover hidden regularities in seemingly random or anomalous events. Our research demonstrates how these patterns can be utilized to inform decision-making processes and shape effective strategies in fields such as fraud detection, risk management, and human rights monitoring. The study presents a novel framework for integrating statistical anomaly detection with strategic planning, allowing organizations to proactively address potential threats and opportunities. Our findings suggest that a deeper understanding of deviant patterns can lead to more robust and adaptive strategies, particularly in complex and uncertain environments. This work contributes to the growing body of literature on data-driven strategy formulation and offers practical insights for policymakers and business leaders.
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