Divisions within the US population on social and political issues have increased by 64% since 1988, with almost all this coming after 2008, according to a study tracking polarization from the end of ...
Almost all the rise in US polarisation over political issues since the eighties occurred from 2008 onwards, a new study suggests. Far more Americans now adopt party and ideological labels in line with ...
Abstract: Traditional k-means clustering is widely used to analyze regional and temporal variations in time series data, such as sea levels. However, its accuracy can be affected by limitations, ...
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 ...
Cryptography secures communication in banking, messaging, and blockchain. Good algorithms (AES, RSA, ECC, SHA-2/3, ChaCha20) are secure, efficient, and widely trusted. Bad algorithms (DES, MD5, SHA-1, ...
Abstract: This paper proposes an improved K-means clustering algorithm based on density-weighted Canopy to address the efficiency bottlenecks and clustering accuracy issues commonly encountered by ...
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 ...
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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results