Unsupervised anomaly detection machine learning frameworks

Abstract

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing anomaly detection operations. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform anomaly detection operations by using a three-tiered unsupervised anomaly detection machine learning framework to perform high-volume anomaly detection and via utilizing a first tier anomaly detection tier that uses a randomized partitioning anomaly detection machine learning model (e.g., an isolation forest anomaly detection machine learning model), a second tier anomaly detection tier that uses a rule-based partitioning anomaly detection model, and a third tier anomaly detection tier that uses a clustering machine learning model and intra-cluster inferences performed based at least in part on cluster distribution ratios and/or per-cluster anomaly designations.