However, traditional unsupervised clustering algorithms (such as K-means, DBSCAN ... more competitive in large-scale data analysis. WiMi’s quantum-assisted unsupervised data clustering technology ...

The final, formatted version of the article will be published soon. Rocky high steep slopes are prone to slope instability, resulting in serious disasters like landslides and avalanches. In practical ...

ABSTRACT: As a highly contagious respiratory disease, influenza exhibits significant spatiotemporal fluctuations in incidence, posing a persistent threat to public health and placing considerable ...

Hut 8 Corp., a major player in cryptomining and high-performance computing (HPC), has launched its new Vega data center in the Texas Panhandle. The property, which spans 162,000 square feet, was ...

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: 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, ...

Data set showcases protein consumption from 25 European countries. The data includes protein consumption (in grams per person per day) for red Meat, white meat, eggs, milk, fish, cereal, starch, nuts, ...

Abstract: in the era of big data, international education has accumulated a lot of relevant data, which can produce various research values. The Corona Virus Disease 2019 (COVID-19) seriously affects ...

In cognitive diagnostic assessment (CDA), clustering analysis is an efficient approach to classify examinees into attribute-homogeneous groups. Many researchers have proposed different methods, such ...