Unsupervised learning

Unsupervised learning is used when we don't have the corresponding target output values for the input. It is used to understand the data distribution and discover similarity of some kinds between the data points. As there is no target output to learn from, unsupervised algorithms rely on initializers to generate initial decision boundaries and update them as they go through the data. After going through the data multiple times, the algorithms update to optimized decision boundaries, which groups data points based on similarities. This method is known as clustering, and algorithms such as k-means are used for it.