This type of machine learning involves training an algorithm on an untitled dataset, where there are no known output values. The algorithm learns to identify patterns and relationships within the input data and can be used for tasks such as clustering and anomaly detection. Some examples of unsupervised learning methods include k-means clustering, principal component analysis, and association rule mining. Examples of unsupervised Machine learning algorithms are Principal component analysis (PCA) and Independent component analysis (ICA).