Abstract: The K-Means clustering algorithm is proposed by Mac Queen in 1967 which is a partition-based cluster analysis method. It is used widely in cluster analysis for that the K-means algorithm has ...

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 ...

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

As tensions rise around antisemitism in the U.K. and questions mount over Britain’s stance on Israel, Prime Minister Keir Starmer’s Cabinet reshuffle has put a spotlight on some familiar concerns. But ...

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: In this paper, an improved K-means clustering algorithm, EGLK-Means, is proposed, which optimizes the clustering results by enhancing global and local information. The traditional K-means ...

Christy Bieber has a JD from UCLA School of Law and began her career as a college instructor and textbook author. She has been writing full time for over a decade with a focus on making financial and ...

A K-Means algorithm implementation involving various optimization techniques. Used to group MNIST dataset of hand-written numbers 0-9.