Entropy-Based Anomaly Detection for Gaussian Mixture Modeling

Gaussian mixture modeling is a generative probabilistic model that assumes the observed data are generated from of multiple distributions. This provides flexible approach to complex distributions may not be easily represented by single distribution. The with noise component refers finite includes an additional background or outliers in data. helps take into account presence anomalies latter aspect crucial for anomaly detection situations where clear, early warning abnormal condition required. paper proposes novel entropy-based procedure initializing models. Our shown easy implement and effective detection. We successfully identify both simulated real-world datasets, even significant levels outliers. provide step-by-step description proposed analysis process, along corresponding R code, which publicly available GitHub repository.