Applied Unsupervised Learning with Python

Chapter 3

Neighborhood Approaches and DBSCAN

Learning Objectives

By the end of this chapter, you will be able to:

• Understand how neighborhood approaches to clustering work from beginning to end
• Implement the DBSCAN algorithm from scratch by using packages
• Identify the best suited algorithm from k-means, hierarchical clustering, and DBSCAN to solve your problem

In this chapter, we will have a look at DBSCAN clustering approach that will serve us best in the highly complex data.

Introduction

So far, we have covered two popular ways of approaching the clustering problem: k-means and hierarchical clustering. Both clustering techniques have pros and cons associated with how they are carried out. Once again, let's revisit where we have been ...