This project used a Kmeans after PCA model to segment retail customers to optimize marketing efforts. When the model repeatedly returned a single cluster, the model was used to prove the customers' ...
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, ...
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 ...
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 ...
This study investigates the application of advanced clustering methods to geological fracture analysis in the Baba Kohi anticline, located in the folded Zagros region of southwest Iran. The primary ...
Dr. Means, President Trump’s new pick for surgeon general, has focused on the prevalence of chronic diseases and called on the government to scale back on childhood vaccines. By Benjamin Mueller 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 ...
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 ...
This project consists in the implementation of the K-Means and Mini-Batch K-Means clustering algorithms. This is not to be considered as the final and most efficient algorithm implementation as the ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback