Scientists are trying to tame the chaos of modern artificial intelligence by doing something very old fashioned: drawing a table. Instead of chemical elements, the new chart arranges learning ...

Abstract: In this paper, an improved K-means clustering algorithm, EGLK-Means, is proposed, which optimizes the clustering results by enhancing global and local information. The traditional K-means ...

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.

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 ...

In this project, I explored the Mall_Customers.csv dataset with the main focus on customer segmentation using K-Means clustering. The goal was to identify distinct customer groups based on Age, Annual ...

This project implements the k-Means Clustering algorithm in Python for clustering datasets with arbitrary features and cluster counts. It includes two versions: k-Means for 2 features with k=2 ...

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 ...