As I said – Imagine learning without a teacher, with no labelled inputs or help from the user.
We have taken an unlabeled raw input data, which means it is not categorized and corresponding outputs are also not given. Now, this unlabeled input data is fed to the machine learning model in order to train it. The process is illustrated in the image the data is interpreted and then we apply the most appropriate algorithm, after which the data is processed and it yields classified outputs. (image attached)
There are two types of unsupervised learning.
(1) Clustering: Here we form clusters of similar data (like in the example).
(2) Association: It is also an important topic in data mining. Association analysis attempts to find relationships between different entities. The classic example of association rules is market basket analysis. This means using a database of transactions in a supermarket to find items that are bought together. For example, a person who buys potatoes and burgers usually buys beer.