Unsupervised Learning

What is unsupervised learning?

In supervised learning, we discussed that the models (or classifiers) are built after training data, and attributes are linked to the target attribute (or label). These models are then used to predict the values of the class attribute in test or future data instances. Unsupervised learning, however, is different. There’s no target or class attribute. The methods of unsupervised learning are used to find underlying patterns in data and are often used in exploratory data analysis.

In unsupervised learning, the data is not labeled. The methods instead focus on the data’s features. The overall goal of the methods is to find relationships within the data and group data points based on some similarity matrix.