The K-Means algorithm is one of the most sophisticated and known algorithms for data-clustering. In this study, we will show the K-Means algorithm as it relates to OpenCL, which is a widespread ...

Currently, a wide array of clustering algorithms have emerged, yet many approaches rely on K-means to detect clusters. However, K-means is highly sensitive to the selection of the initial cluster ...

This useful study presents a biologically realistic, large-scale cortical model of the rat's non-barrel somatosensory cortex, investigating synaptic plasticity of excitatory connections under varying ...

Learn how to handle outliers in k-means clustering using different approaches, such as detection, removal, clustering, transformation, and validation.

Use of an electronic medication management application to support Pharmacists Review to Optimise Medicines in Residential Aged Care (PROMPT-RC): a study protocol for a parallel cluster randomised ...

Contribute to MatteoSpataro/Parallel-K-means development by creating an account on GitHub.

You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs ...