Nature

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the ...
Visual Studio Magazine

K-Means Data Clustering from Scratch Using C#

K-means is comparatively simple and works well with large datasets, but it assumes clusters are circular/spherical in shape, so it can only find simple cluster geometries. Data clustering is the ...
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, ...
Nature

A weight clustering algorithm based on sliding window model for stream data

Streaming data, characterized by its temporal variations and large volumes, presents unique challenges for clustering tasks. To address these challenges, this paper proposes a novel weighted ...
EurekAlert!

The first scalable motif-based graph local clustering algorithm for real-world massive graphs

Motif-based graph local clustering is a popular method for graph mining tasks due to its various applications, such as community detection, network optimization and graph learning. However, the ...