The K-shaped economy has returned, with gaps widening between high and low-income Americans. Higher earners see steadier opportunities, while lower-income households face cooler prospects. Some ...

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, ...

Amsterdam’s struggles with its welfare fraud algorithm show us the stakes of deploying AI in situations that directly affect human lives. What Amsterdam’s welfare fraud algorithm taught me about fair ...

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: Accurate offset measurement is crucial for recovering the size of past earthquakes and understanding the recurrence patterns of strike-slip faults. Traditional methods, which rely on manual ...

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.

Abstract: The k-means algorithm is a widely used Machine learning algorithm for clustering. This paper introduces a parallel k-means algorithm implementation for image classification. We implemented ...

Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States Laufer Center for ...