Skip to Main Content
The Unsupervised Learning Workshop
book

The Unsupervised Learning Workshop

by Aaron Jones, Christopher Kruger, Benjamin Johnston, Richard Brooker, John Wesley Doyle, Priyanjit Ghosh, Sani Kamal, Ashish Pratik Patil, Philip Solomon, Geetank Raipuria
July 2020
Intermediate to advanced content levelIntermediate to advanced
550 pages
9h 58m
English
Packt Publishing
Content preview from 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 ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

The Supervised Learning Workshop

The Supervised Learning Workshop

Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur
The Machine Learning Workshop - Second Edition

The Machine Learning Workshop - Second Edition

Hyatt Saleh, John Wesley Doyle, Akshat Gupta, Harshil Jain, Vikraman Karunanidhi, Subhojit Mukherjee, Madhav Pandya, Subhash Sundaravadivelu

Publisher Resources

ISBN: 9781800200708Supplemental Content