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This project consists in the implementation of the K-Means and Mini-Batch K-Means clustering algorithms. This is not to be considered as the final and most efficient algorithm implementation as the objective here is to make a clear comparison between the sequential and parallel execution of the clustering steps.

License: GNU General Public License v2.0

CMake 5.14% Cuda 47.26% C++ 47.60%
benchmark centroids clustering clusters euclidean-distances gpu gpu-programming k-means k-means-clustering machine-learning

k-means's Introduction

scrayil@h1dd3n:~$ profile

Hi there, i am Mattia, also known as Scrayil 👋

  • 🖥️ IT Engineer.
  • 📚 I’m currently studying artificial intelligence at university.
  • 🌱 I’m currently learning cybersecurity by myself, in my free time.
  • 💬 Ask me about any topic.
  • 😄 Pronouns: He/Him

How to reach me:

                                  

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