- âś•This summary was generated using AI based on multiple online sources. To view the original source information, use the "Learn more" links.
K-Means clustering is an unsupervised machine learning technique used to partition data into K clusters. Each cluster contains data points that are more similar to each other than to those in other clusters. This method is widely used due to its simplicity and efficiency.
Understanding the K-Means Algorithm
The K-Means algorithm involves the following steps:
Initialization: Randomly select K centroids.
Expectation Step: Assign each data point to the nearest centroid.
Maximization Step: Recompute the centroids as the mean of all points in each cluster.
Iteration: Repeat the expectation and maximization steps until the centroids converge.
The quality of the clustering is measured using the sum of squared errors (SSE), which is the sum of the squared distances between each point and its nearest centroid. The goal is to minimize this value.
Implementing K-Means Clustering in Python
K-Means Clustering in Python: A Practical Guide
Jul 20, 2020 · Learn how to perform k-means clustering in Python with scikit-learn, a popular machine learning library. This tutorial covers the basics of k-means, how to choose the number of clusters, and …
See results only from realpython.comLearning Paths
Take your coding skills to the next level with our Python roadmap including learning …
Office Hours
Real Python Office Hours Your Real Python membership includes access to “Office …
Code Mentor
Intelligent learning tools for instant code explanations, reviews, and personalized …
API
Python API Tutorials You design, build, and integrate APIs in Python. Learn REST and …
Community Chat
Join the Real Python Community Chat and meet the Real Python Team and other …
Machine-Learning
Learn how to implement machine learning (ML) algorithms in Python. With these …
Ipython
When you’re executing Python code, the standard Python shell that comes with …
Python Books by Real Pyth…
Python Books Published by Real Python Take your Python skills to the next level …
Performing Cluster Analysis in Python: A Step-by-Step …
Sep 27, 2024 · Performing the K-means clustering algorithm in Python is straightforward thanks to the scikit-learn library. Indeed, we have already done this …
The Beginner’s Guide to Clustering with Python
Apr 3, 2025 · This article provides a practical hands-on introduction to common clustering methods that can be used in Python, namely k-means clustering and …
2.3. Clustering — scikit-learn 1.8.0 documentation
Clustering of unlabeled data can be performed with the module sklearn.cluster. Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on trai...
How to Build a Clustering Model in Python: Complete …
Aug 30, 2025 · Build a clustering model in Python with Google Colab—K-Means, DBSCAN & Hierarchical explained step by step with code and examples. Clustering …
Python Machine Learning - K-means - W3Schools
The algorithm iteratively divides data points into K clusters by minimizing the variance in each cluster. Here, we will show you how to estimate the best value for …
Clustering with Confidence: A Practical Guide to Data …
Jun 10, 2024 · This step can guide you in choosing the appropriate clustering algorithm and the number of clusters. Code Example: Here’s a Python code snippet …
Learn Clustering in Python – A Machine Learning …
Feb 5, 2025 · In this comprehensive handbook, we’ll delve into the must-know clustering algorithms and techniques, along with some theory to back it all up. …
Clustering in Python: A Comprehensive Guide - CodeRivers
Apr 20, 2025 · Clustering in Python is a powerful tool for exploring and understanding data. By mastering the fundamental concepts, using the right libraries, following common and best practices, and …
Introduction to k-Means Clustering with scikit-learn in …
Mar 10, 2023 · In this tutorial, learn how to apply k-Means Clustering with scikit-learn in Python
Skip to content
Accessibility Feedback