Abstract: For radar signal sorting based on pulse descriptors, the inherent limitations of the traditional K-means algorithm include the requirement of a predefined number of clusters, the sensitivity ...

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 ...

Accurately identifying fracture zones and their types in strata is of great significance for enhancing oil and gas recovery efficiency. Due to its complicated geological structure and long-term ...

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 ...

A K-Means algorithm implementation involving various optimization techniques. Used to group MNIST dataset of hand-written numbers 0-9.

The K-Means Visualizer is an interactive web application designed to help users understand and visualize the K-Means clustering algorithm. Through an intuitive interface, users can experiment with ...

We investigate the role of the initialization for the stability of the k-means clustering algorithm. As opposed to other papers, we consider the actual k-means algorithm (also known as Lloyd algorithm ...