Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...
A bill aimed at keeping cellphones out of classrooms passed the Michigan House of Representatives on Tuesday, marking a comeback for legislation that failed last year. The bill, sponsored by Rep. Mark ...
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, ...
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 ...
Advanced K-Means clustering system for customer analytics and segmentation using machine learning. Includes RFM analysis, business insights, and actionable marketing strategies. - ...
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 ...
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 ...
Abstract: This paper introduces a codebook-based trellis-coded quantization (TCQ) approach utilizing K-means clustering, designed specifically for massive multiple-input multiple-output systems. The ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results