Anomaly Detection Algorithm for Elliptical Clusters Based On Maximum Cluster Diameter Criteria

Abstract Anomaly detection, a fundamental task in data analysis, requires robust mathematical frameworks. We present to you a pioneering anomaly detection algorithm poised to redefine the landscape of data analysis. Within this study, we introduce a novel approach meticulously tailored to the nuanced characteristics of elliptical clusters, showcasing an integration of sophisticated geometric and statistical methodologies. Leveraging robust mathematical frameworks, our algorithm transcends conventional boundaries, promising unprecedented levels of accuracy and efficiency in anomaly detection tasks. Through the convergence of theoretical insights and practical applications, our research heralds a paradigm shift in the domain of data analytics, offering a profound advancement in the identification and interpretation of anomalous data points within complex datasets. Subject Descriptors: Anomaly Detection, Data Clustering Categories: I.2.10 [Artificial Intelligence]: Vision and Scene Understanding—Statistical Methods