GitHub

mini-batch-kmeans

This project used a Kmeans after PCA model to segment retail customers to optimize marketing efforts. When the model repeatedly returned a single cluster, the model was used to prove the customers' ...
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, ...
Scientific Research Publishing

Ng, H.P., Ong, S.H., Foong, K.W.C., Goh, P.S. and Nowinski, W.L. Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm. 2006 IEEE Southwest ...

ABSTRACT: 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 ...
Scientific Research Publishing

Applying K-Means Clustering and Fuzzy C-Means Clustering in Vehicle Crashes ()

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 ...
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, ...
GitHub

k-means-clustering

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 ...
IEEE

K-means clustering algorithm and Python implementation

Abstract: K-means is a commonly used algorithm in machine learning. It is an unsupervised learning algorithm. It is regularly used for data clustering. Only the number of clusters are needed to be ...