Anomaly Clustering

Anomaly clustering aims to group different types of anomalous data points within a dataset, going beyond simple anomaly detection which only distinguishes anomalies from normal data. Current research focuses on developing unsupervised methods, often employing deep learning for feature extraction and representation learning, combined with clustering algorithms like k-Nearest Neighbors or probabilistic mixture models, to effectively identify and group subtle anomalies even in complex data like images and video. This field is significant because it enables a more nuanced understanding of anomalous behavior, improving the accuracy and interpretability of anomaly detection systems across diverse applications such as industrial quality control, network security, and medical diagnosis.