Summary

In this chapter, we learned the basics of how clustering works. Clustering is a form of unsupervised learning where the features are given, but not the labels. It is the goal of the clustering algorithms to find the labels based on the similarity of the data points.

We also learned that there are two types of clustering, flat and hierarchical, with the first type requiring the number of clusters to find, whereas the second type finds the optimal number of clusters itself.

The k-means algorithm is an example of flat clustering, whereas mean shift and agglomerative hierarchical clustering are examples of a hierarchical clustering algorithm.

We also learned about the numerous scores to evaluate the performance of a clustering model, with the labels in the extrinsic approach or without the labels in the intrinsic approach.

In Chapter 6, Neural Networks and Deep Learning, you will be introduced to a field that has become popular in this decade due to the explosion...