Advances in recent techniques for scientific data collection in the era of big data allow for the
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …

K-means is a fundamental clustering algorithm widely used in both academic and industrial
applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the …

Recent advances in generative modeling have led to an increased interest in the study of
statistical divergences as means of model comparison. Commonly used evaluation …

This study presents a framework for predicting geological characteristics based on
integrating a stacking classification algorithm (SCA) with a grid search (GS) and K-fold cross …

The 'You Only Look Once'v3 (YOLOv3) method is among the most widely used deep
learning-based object detection methods. It uses the k-means cluster method to estimate the …

Machine learning approaches are becoming increasingly popular to improve the efficiency
of specific emitter identification (SEI). However, in most non-cooperative SEI scenarios …

This paper presents a novel accelerated exact k-means called as" Ball k-means" by using
the ball to describe each cluster, which focus on reducing the point-centroid distance …

We investigate coresets-succinct, small summaries of large data sets-so that solutions found
on the summary are provably competitive with solution found on the full data set. We provide …

This paper presents a novel accelerated exact-means called as “Ball-means” by using the
ball to describe each cluster, which focus on reducing the point-centroid distance …

Clustering is the most fundamental technique for data processing. This paper presents a
collaborative annealing power k-means++ clustering algorithm by integrating the k-means++ …