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, ...

Abstract: In order to solve the problem that the initial K value of K-Means clustering algorithm needs to be given in advance and the division needs to be determined according to the initial ...

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 K-means algorithm, one of the most well-known clustering techniques, has been widely employed to solve a variety of problems. In contrast, the k-means clustering algorithm has numerous ...

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 ...