This study investigates the application of advanced clustering methods to geological fracture analysis in the Baba Kohi anticline, located in the folded Zagros region of southwest Iran. The primary ...

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 ...

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, ...

ABSTRACT: From the perspective of student consumption behavior, a data-driven framework for screening student loan eligibility was developed using K-means clustering analysis and decision tree models.

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 ...

Introduction: In unsupervised learning, data clustering is essential. However, many current algorithms have issues like early convergence, inadequate local search capabilities, and trouble processing ...

Abstract: This paper presents an accelerated spherical Kmeans clustering algorithm for large-scale and high-dimensional sparse document data sets. We design an algorithm working in an ...

ABSTRACT: As a highly contagious respiratory disease, influenza exhibits significant spatiotemporal fluctuations in incidence, posing a persistent threat to public health and placing considerable ...

Abstract: The palette mode is a specialized coding tool for coding screen content video in Alliance for Open Media Video 1 (AV1), and K-means clustering is a necessary step in the palette mode.