  Unsupervised learning is a discipline of machine learning which aims at
discovering patterns in big data sets or classifying the data into several
categories without being trained explicitly. We show that unsupervised learning
techniques can be readily used to identify phases and phases transitions of
many body systems. Starting with raw spin configurations of a prototypical
Ising model, we use principal component analysis to extract relevant low
dimensional representations the original data and use clustering analysis to
identify distinct phases in the feature space. This approach successfully finds
out physical concepts such as order parameter and structure factor to be
indicators of the phase transition. We discuss future prospects of discovering
more complex phases and phase transitions using unsupervised learning
techniques.
