This repository contains computational notebooks and analysis code for research on smart K-means clustering algorithms applied to social exclusion indicators. The project implements and compares ...

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, ...

In financial filings, Casey Means stated that she would liquidate holdings in companies that sell personal devices, supplements, tobacco and tech. By Christina Jewett and Benjamin Mueller See more of ...

Clustering is an unsupervised machine learning technique used to organize unlabeled data into groups based on similarity. This paper applies the K-means and Fuzzy C-means clustering algorithms to a ...

ABSTRACT: Domaining is a crucial process in geostatistics, particularly when significant spatial variations are observed within a site, as these variations can significantly affect the outcomes of ...

Abstract: The traditional K-means algorithm often leads to unstable clustering quality due to the randomness of the initial clustering center selection and tends to fall into suboptimal solutions when ...

Abstract: This paper introduces EvoK-Means, a Differential Evolution-optimized K-Means algorithm for improved clustering of electricity consumption data. Traditional methods like K-Means and DBSCAN ...

This project consists in the implementation of the K-Means and Mini-Batch K-Means clustering algorithms. This is not to be considered as the final and most efficient algorithm implementation as the ...