Genomic tool untangles how microbes spread—even when they look almost identical

Researchers have developed a powerful new tool that can track how microbes spread between people with unprecedented precision ...
Thorax

At-risk registers integrated into primary care to stop asthma crises in the UK: cluster randomised controlled trial with economic evaluation

Background A regional trial indicated that implementing at-risk asthma registers in primary care could reduce hospital ...
GitHub

MidiSeparator-Simple K-means Clustering.pd

Using the visual programming language Pure Data, I created a tough, low-level K-Clustering algorithm. This refers to an unsupervised AI algorithm that identifies bundles of data points from one source ...
Scientific Research Publishing

G. Jagannathan, K. Pillaipakkamnatt and R. Wright, “A New Privacy Preserving Distributed k-Clustering Algorithm,” In Proceeding of the 6th SIAM International Conference on ...

ABSTRACT: Data mining has been a popular research area for more than a decade. There are several problems associated with data mining. Among them clustering is one of the most interesting problems.
Scientific Research Publishing

Liu, C.Y. (2025) Distributed Energy Storage Cluster Division Method Based on Improved K-Means Clustering Algorithm. Northeast Power Technology, 46, 1-5.

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

Research on Improved k-Means Clustering Algorithm Based on Density-Weighted Canopy

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 ...
IEEE

Examining the Impact of Distance-Based Similarity Metrics on the Performance of Projected Clustering Algorithm for Fingerprint Database Clustering

Abstract: The projected clustering (PROCLUS) algorithm is a subspace clustering algorithm based on the k-medoids clustering approach. It is designed to address the challenges of irrelevant received ...