The Unsupervised Learning Workshop

3. Neighborhood Approaches and DBSCAN

Overview

In this chapter, we will see how neighborhood approaches to clustering work from start to end and implement the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm from scratch by using packages. We will also identify the most suitable algorithm to solve your problem from k-means, hierarchical clustering, and DBSCAN. By the end of this chapter, we will see how the DBSCAN clustering approach will serve us best in the sphere of highly complex data.

Introduction

In previous chapters, we evaluated a number of different approaches to data clustering, including k-means and hierarchical clustering. While k-means is the simplest form of clustering, it is still extremely ...