Detecting delays, anomalous work handovers, and high workloads is a challenging process mining task that is typically performed at the case level. However, process mining users would benefit from analyzing such behaviors at the process level where instances of such behavior are called high-level events. We propose a novel framework for high-level event mining that leverages anomaly detection and clustering methods to identify and analyze high-level events in an unsupervised setting. Our framework, called High-level Event Mining Machine Learning Approach (HEMMLA), utilizes an autoencoder-based anomaly detection method and requires no predefined time window or anomaly thresholds. An extensive experimental evaluation over real and synthetic datasets highlights the high scalability of our approach. An additional user study over real datasets underlines the ability of our framework to detect more interesting and explainable anomalies than the state-of-the-art.