Clustering with Scikit-Learn: a Gentle Introduction

Author(s): Riccardo Andreoni

Originally published on Towards AI.

Learn how to apply state-of-the-art clustering algorithms efficiently and boost your machine-learning skills.
Image source: unsplash.com.

You find yourself in a vast library with countless books scattered on the shelves. Each book is a unique piece of information, and your goal is to organize them based on their characteristics. As you wander through the shelves, you notice that some books share similar themes or topics. You are certainly capable of identifying groups of related books. This is called clustering.

In Data Science, clustering is used to group similar instances together, discovering patterns, hidden structures, and fundamental relationships within a dataset.

In this introduction guide, I will formally introduce you to clustering in Machine Learning. I will present the theory of the most used clustering models, and we will understand how to practically implement them with Scikit-Learn. With a hands-on approach, you will find plenty of code and plots to familiarize yourself with clustering: a must-have tool for every data scientist.

Clustering in Machine Learning stands as a fundamental unsupervised learning task, different from its supervised counterparts due to the lack of labeled data. Unlike classification algorithms, such as Random Forest or Support Vector Machines, whose training relies on labeled data points, clustering algorithms work on unlabeled data, aiming to discover the structures and patterns inside the dataset.

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Published via Towards AI