So far, we have focused our attention exclusively on supervised learning problems, where every data point in the dataset had a known label or target value. However, what do we do when there is no known output or no teacher to supervise the learning algorithm?

This is what unsupervised learning is all about. In unsupervised learning, the learning process is shown only in the input data and is asked to extract knowledge from this data without further instruction. We have already talked about one of the many forms that unsupervised learning comes in—dimensionality reduction. Another popular domain is cluster analysis, which aims to partition data into distinct groups of similar items.

Some of the problems where clustering techniques can be useful are document analysis, image retrieval, finding spam emails, identifying fake news, identifying criminal activities, and so on.

In this chapter, we want to understand how different clustering algorithms can be used to extract hidden structures in simple, unlabeled datasets. These hidden structures have many benefits, whether they are used in feature extraction, image processing, or even as a preprocessing step for supervised learning tasks. As a concrete example, we will learn how to apply clustering to images to reduce their color spaces to 16 bits. 

More specifically, we will cover the following topics:

• k-means clustering and expectation-maximization and implementing these in OpenCV
• Arranging clustering algorithms in hierarchical trees and what are the benefits that come from that
• Using unsupervised learning for preprocessing, image processing, and classification

Let's get started!