Summary

In this chapter, we learned how clustering works. Clustering is a form of unsupervised learning, where the features are given, and the clustering algorithm finds the labels.

There are two types of clustering: flat and hierarchical.

The k-means algorithm is a flat clustering algorithm, where we determine K center points for our K clusters, and the algorithm finds the data points.

Mean Shift is an example of a hierarchical clustering algorithm, where the number of distinct label values is to be determined by the algorithm.

The final chapter will introduce a field that has become popular this decade due to the explosion of computation power and cheap, scalable online server capacity. This field is the science of neural networks and deep learning.