Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.
mathworks.com/discovery/unsupervised-learning.html
In other pattern recognition problems, the training data consists of a set of input
vectors x without any corresponding target values.
The goal in such unsupervised learning problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization.
Christopher M. Bishop - «Pattern Recognition and Machine Learning» (2006)