Microsoft

Infostealers without borders: macOS, Python stealers, and platform abuse

How modern infostealers target macOS systems, leverage Python‑based stealers, and abuse trusted platforms and utilities to ...
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

️ An intelligent system that takes a document and classifies different writing styles within the document using stylometric techniques.
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, ...
AOL

A growing number of Americans are using benefits to get weight-loss drugs, but the cost is weighing on employers. What that means for everyone else

Weight-loss drugs are booming (1) and you’ve probably heard of them: Wegovy, Ozempic, Mounjaro, Zepbound. These GLP-1 medications are helping Americans slim down and manage diabetes — and in some ...
Frontiers

Efficient vegetation filtering method using K-means clustering algorithm: case study of high steep rock slope

Rocky high steep slopes are among the most dangerous disaster-causing geological bodies in large-scale engineering projects, like water conservancy and hydropower projects, railway tunnels, and metal ...
Frontiers

Profiling investor behavior in the Malaysian derivatives market using K-means clustering

1 Department of Applied Sciences, Intelligent Asset Reliability Centre, Institute of Emerging Digital Technologies, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia 2 Bursa Malaysia Berhad, ...
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

Jain, A. K. (2010). Data Clustering: 50 Years Beyond K-means. Pattern Recognition Letters, 31, 651-666.

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 ...