In order to explore the medication rules of Shang Han Lun, this article conducted complex network analysis and cluster ...
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, ...
This is an important work implementing data mining methods on IMC data to discover spatial protein patterns related to the triple-negative breast cancer patients' chemotherapy response. The evidence ...
GATE Data Science & Artificial Intelligence (DA) Important Questions: GATE Data Science & Artificial Intelligence (DA) ...
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 ...
1 Department of Applied Sciences, Intelligent Asset Reliability Centre, Institute of Emerging Digital Technologies, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia 2 Bursa Malaysia Berhad, ...
Democratic New York City mayoral candidate and self-described socialist Zohran Mamdani is facing criticism over remarks made at a Young Democratic Socialists of America conference, where he urged ...
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 ...
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