Chapter 8: Clustering Analysis

Finally, you have made your way to the last chapter of the second part of this book. Clustering analysis is another useful and popular algorithmic pattern recognition tool. When performing classification or prediction, the algorithms find the patterns that help create a relationship between the independent attributes and the dependent attribute. However, clustering does not have a dependent attribute, so it does not have an agenda in pattern recognition. Clustering is an algorithmic pattern recognition tool with no prior goals. With clustering, you can investigate and extract the inherent patterns that exist in a dataset. Due to these differences, classification and prediction are called supervised learning, while clustering is known as unsupervised learning.

In this chapter, we will use examples to fundamentally understand clustering analysis. Then, we will learn about the most popular clustering algorithm: K-Means. We will also perform some K-Means...