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, ...

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, ...

Mr. Means quietly departed his federal role about a month ago. His sister has been nominated for surgeon general. By Benjamin Mueller Calley Means, an influential adviser to Health Secretary Robert F.

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 ...

1 School of Computer Science and Technology, Yibin University, Yibin, China 2 School of Computer and Software, Southwest Petroleum University, Chengdu, China Ever since Density Peak Clustering (DPC) ...

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 ...

A high-performance Parallel K-Means Clustering algorithm implemented in C++ with OpenMP for parallelization. This project demonstrates the use of advanced clustering techniques with efficient ...

ABSTRACT: The use of machine learning algorithms to identify characteristics in Distributed Denial of Service (DDoS) attacks has emerged as a powerful approach in cybersecurity. DDoS attacks, which ...

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 ...