The New York Times

U.K. Allows U.S. to Use Bases to Hit Iranian Forces Menacing Strait Traffic

Prime Minister Keir Starmer has vowed not to be dragged into the war with Iran, but his government described its new position as essentially defensive. By Michael D. Shear Reporting from London ...
GitHub

Ballot: Balanced k-means clustering with optimal transport

Ballot (Balanced Lloyd with Optimal Transport) is a high-performance Python package for balanced clustering. It solves the problem of creating equal-sized clusters (or clusters with specific capacity ...
Visual Studio Magazine

Anomaly Detection Using K-Means Clustering with JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of anomaly detection using k-means data clustering, implemented with JavaScript. Compared to other anomaly detection techniques, ...
GitHub

IfyQ/DS_Hierarchical_Clustering

This project applies hierarchical clustering to group local authorities in England based on case closure reasons from the Children in Need Census (2013–2024). It supports benchmarking, policy ...
Nature

Hyperspectral imaging and K-means clustering for material structure classification and detection of unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have become increasingly widespread in a variety of industries due to their versatility and efficiency in applications such as agriculture, surveillance, logistics, and ...
Scientific Research Publishing

Tabianan, K., Velu, S. and Ravi, V. (2022) K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data. Sustainability, 14, Article ...

ABSTRACT: Doping is an issue associated with elite sports as athletes attempt to enhance their performance to gain an edge over other athletes. However, the prevalence of doping is continuously ...
Nature

Image tampering detection using dynamic histogram equalization based LIOP features and novel scaled K-means + + clustering

Digital images are one of the most regularly used means of storing personal memories and legal proofs. Content modification of digital images is becoming more and more common due to highly advanced ...
LinkedIn

Day 34: Introduction to Clustering — K-Means Basics

Clustering works much like that: it automatically groups similar items together based on their features. Today, on Day 34 of “100 Days of Data Science,” we’ll explore K-Means clustering — a ...
IEEE

A Topological-Indicators-Based k-Means Clustering Algorithm and Its Application in Time Series Data: A Case Study on Sea Level Variability in Peninsular Malaysia

Abstract: Traditional k-means clustering is widely used to analyze regional and temporal variations in time series data, such as sea levels. However, its accuracy can be affected by limitations, ...
IEEE

Codebook-Based Trellis-Coded Quantization Scheme Using K-Means Clustering for Massive MIMO Systems

Abstract: This paper introduces a codebook-based trellis-coded quantization (TCQ) approach utilizing K-means clustering, designed specifically for massive multiple-input multiple-output systems. The ...
LinkedIn

Identifying Customer Spending Patterns Using K-Means Clustering

Understanding customer spending patterns is crucial for businesses to improve targeted marketing, customer segmentation, and sales forecasting. K-Means Clustering, an unsupervised machine learning ...