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, ...

Abstract: At present, the decision-making and optimization power of big data makes daily production, operation, and decision-making more efficient and intelligent. With the rise of intelligence level, ...

ABSTRACT: From the perspective of student consumption behavior, a data-driven framework for screening student loan eligibility was developed using K-means clustering analysis and decision tree models.

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 ...

As the Bay Area’s housing crisis continues to intensify, new data shows just how hard it is to even qualify as middle-class anymore. Across several California counties, the threshold for being a ...

This study employs an unsupervised machine learning model to analyze the level of prosperity across countries based on the 2023 Legatum Prosperity Index data. The dataset includes various economic and ...

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 ...

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 ...