Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Data Science for Decision Makers

You're reading from   Data Science for Decision Makers Enhance your leadership skills with data science and AI expertise

Arrow left icon
Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781837637294
Length 270 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Jon Howells Jon Howells
Author Profile Icon Jon Howells
Jon Howells
Arrow right icon
View More author details
Toc

Table of Contents (20) Chapters Close

Preface 1. Part 1: Understanding Data Science and Its Foundations
2. Chapter 1: Introducing Data Science FREE CHAPTER 3. Chapter 2: Characterizing and Collecting Data 4. Chapter 3: Exploratory Data Analysis 5. Chapter 4: The Significance of Significance 6. Chapter 5: Understanding Regression 7. Part 2: Machine Learning – Concepts, Applications, and Pitfalls
8. Chapter 6: Introducing Machine Learning 9. Chapter 7: Supervised Machine Learning 10. Chapter 8: Unsupervised Machine Learning 11. Chapter 9: Interpreting and Evaluating Machine Learning Models 12. Chapter 10: Common Pitfalls in Machine Learning 13. Part 3: Leading Successful Data Science Projects and Teams
14. Chapter 11: The Structure of a Data Science Project 15. Chapter 12: The Data Science Team 16. Chapter 13: Managing the Data Science Team 17. Chapter 14: Continuing Your Journey as a Data Science Leader 18. Index 19. Other Books You May Enjoy

Clustering – unveiling hidden patterns in your data

Clustering is a powerful tool in the UL toolkit. But what is it, and how can it help decision-makers in business? Let’s dive in.

What is clustering?

Clustering is a method of UL that involves grouping data points together based on their similarity. Unlike SL, where we have a clear target or outcome variable, UL (and, by extension, clustering) is all about finding hidden structures and patterns in data without any predefined labels.

Think of clustering as a way to discover and explore unknown territories in your data. It’s like an explorer setting out on a journey without a map, using only their observations to make sense of the landscape.

How does clustering work?

The process of clustering involves several steps:

  1. Feature selection

    In this step, you choose the characteristics or attributes of your data that you believe can help differentiate between different groups. For example, if you’...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime