As the name suggests, this class of algorithms learns/infers concepts without supervision. Unlike supervised learning algorithms, which infer a mapping function based on training dataset consisting of input data points and output signals, unsupervised algorithms are tasked with finding patterns and relationships in the training data without any output signals available in the training dataset. This class of algorithms utilizes the input dataset to detect patterns, and mine for rules or group/cluster data points so as to extract meaningful insights from the raw input dataset.

Unsupervised algorithms come in handy when we do not have the liberty of a training set that contains corresponding output signals or labels. In many real-world scenarios, datasets are available without output signals and it is difficult to manually label them. Thus, unsupervised algorithms are helpful in plugging such gaps.

Similar to supervised learning algorithms, unsupervised algorithms can also be categorized for ease of understanding and learning. The following are different categories of unsupervised learning algorithms.