Unsupervised learning deals with unlabeled data. The objective is to observe the structure of data and find patterns. Tasks like cluster analysis, association rule mining, outlier detection, dimensionality reduction and so on can be modeled as unsupervised learning problems. As the tasks involved in unsupervised learning vary vastly, there is no single process outline that we can follow.
Cluster analysis is a subset of unsupervised learning that aims to create groups of similar items from a set of items. This analysis helps us identify interesting groups of objects that we are interested in. It could be items we encounter in day-to-day life such as movies or songs according to taste, or interests of users in terms of their demography or purchasing patterns.