This project applies hierarchical clustering to group local authorities in England based on case closure reasons from the Children in Need Census (2013–2024). It supports benchmarking, policy ...

Abstract: Hierarchical clustering is a method in data mining and statistics used to build a hierarchy of clusters. Traditional hierarchical clustering relies on a measure of dissimilarity to combine ...

State Key Laboratory of Fine Chemicals, Research and Development Center of Membrane Science and Technology, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China ...

Introduction: The relationship between physical activity and anxiety among students has been extensively studied, with research highlighting the protective effects of physical activity on mental ...

In this tutorial, we will discover how to harness the power of an advanced AI Agent, augmented with both Python execution and result-validation capabilities, to tackle complex computational tasks. By ...

K-Means Clustering Overview K-Means aims to partition your data into K distinct, non-overlapping clusters based on similarity. It minimizes the within-cluster sum of squares (WCSS) — i.e., how close ...

BACKGROUND Leprosy, a neglected tropical disease caused by Mycobacterium leprae, presents significant public health challenges in Brazil due to its slow progression, dermato-neurological ...

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 ...