Another option is to employ generative models such as autoencoders in unsupervised learning. Autoencoders are trained to reconstruct their input data, and if the reconstruction error is higher for a certain data point, it can be classified as an anomaly.

Unsupervised learning algorithms can be used for anomaly detection by first training them on a set of normal data points. During testing, if the algorithm encounters a data point that deviates significantly from the learned normal patterns, it can detect it as an anomaly.

An alternative approach could be to use semi-supervised learning, where a small portion of labeled data is used for training, combined with a large amount of unlabeled data. The algorithm then learns to identify anomalies based on both the labeled and unlabeled data patterns.